Top Talent like Laxman are on Pangea

Pangea, a YC company, connects companies with fractional talent. Fractional hiring allows companies to move faster and work with more specialized talent, while giving talent more flexibility and independence. If you are talent open to fractional work, apply here. If you’re a company looking for high-quality fractional talent, learn more here.

Laxman Tomar

AI & Machine Learning Engineer
Gurugram, HR, IN
Deep Learning
Project Development
Software Development
Natural Language Processing
Machine Learning
Available for hire fromNegotiable
Contracts
Machine Learning Engineer | NLP Specialist
With a Bachelor of Technology in Computer Science and Engineering from Dr. A.P.J. Abdul Kalam Technical University, this professional has a strong educational foundation. Their experience includes roles such as Senior ML Engineer at Jiffy.com, Senior NLP Engineer at Emplay Inc., and Machine Learning Engineer at Albot Technologies, where they led the development of various AI-powered tools and systems. They also contributed to projects at Robofied and Aegisisc Software Solutions Pvt Ltd, showcasing their expertise in speech/voice-based functions, content development, and technical writing. Their work involves integrating generative capabilities of LLMs like GPT-3 & ChatGPT, building conversational search experiences, and developing AI-powered content moderation engines. With a focus on machine learning, natural language processing, and computer vision, they have consistently demonstrated their ability to innovate and deliver impactful solutions.

Projects

Jiffy Copilot

AI Assistant powered by LLMs for shopping products.

Conversational Search Engine

Architected and Built next-gen conversational search products for SAP, P&G and Equinix. Powered by LLMs like GPT-3.5, Chat-GPT and Falcon via LLM Stack including LangChain. Used Retrieval Augmented Generation to answer customer-specific queries using their data. Operates on top of indexed data into Elasticsearch (updates automatically as new data is added) and Web in real-time. Using Websockets and Async technology paired up with streaming responses to provide a seamless search experience. Holds conversational context to answer follow-up search questions using Query Rephrase and Vector Memory Approach. Powered by Prompting and other approaches to reduce Model Hallucinations and PII Issues. Supports multiple tenants through customer-specific configurations. Actively being used in production by SAP, P&G and Equinix etc.See More

Knowledge Search Engine

Architected the System to build composable Microservices for Indexing, Generation, and Search Components. Supports multiple tenants due to hierarchy controlled by API Parameters, Customer Configs, and Environments. All the Enrichment Servies are developed as a Microservice by Docker and orchestrated by Kubernetes. APIs are designed as per OpenAPI Standard to develop REST endpoints for all the services. Logging and Monitoring Services are integrated into each enrichment service for refinement purposes. Currently in Production and scales to ingest, generate and search over 1M+ documents.See More

Question Answer Generation

Generate Question Answer pairs from indexed text documents. Used T5 fine-tuned on SQUAD as Generator to generate questions from the text of ingested documents. Used Roberta fine-tuned on SQUAD as Reader to return answers with scores for generated questions. Ingested generated QA pairs to ElasticSearch Index. Developed Asynchronous Request Handling Mechanism using Celery and RabbitMQ. Developed RESTAPIs using FastAPI served with Docker & deployed via Kubernetes. Developed a monitoring Dashboard via Flower to track requests getting processed in real time. Built Testing Suite for Unit and Stress Testing via Pytest and Locust.See More

Question Answer Evaluation

Evaluate generated Question Answer pairs to filter out low-quality questions. Built & annotated a dataset of low and good-quality QA pairs with Argilla. Adopted techniques like Weak Supervision and Active Labeling to annotate more samples quickly. Generated features attributing to Question Presence, Grammatical Question Structure, and Text Readability. Built a Random Forest Classifier to obtain ~75% accuracy with data & experiment tracking by DVC + MLFlow. Developed Evaluation Suite to identify low-confidence samples via CleanLab & key slices via Snorkel/Sliceline. Performed Interpretability Analysis via SHAP to identify features influencing misclassifications. Evaluation Strategies helped improve the accuracy to ~85%.See More

Content Moderation Engine

AI-powered Content Moderation Engine for Simpplr. Used Jigsaw's Unintended Bias in Toxicity Classification Challenge Dataset comprising ~2M comments. Trained a Mini-LM Model to detect racial, religious hate, insulting, and explicit comments with ~91% accuracy. Developed a Dynamic Thresholding Mechanism, Emojis Decoder, and PII Handler to make the solution robust. Used ONNX Conversion & Dynamic Quantization to make the solution 4x faster with 1/4th less storage space. Served by FastAPI and Docker in production and actively being used by 500+ companies.See More

Email Phishing Detection

Worked on detecting whether an Email in Mailbox is Normal, Spam or Phishing; Performed Extensive Featurization alongwith Transformers based Embeddings to obtain 5% error rate.See More

Network Anaomly Detection

Worked on detecting 15 Types of Network Attacks including DOS, DDOS, Bot, SQL Injection, directly from the Network Interface. Obtained 94% accuracy using Stacked Model.See More

Homoglyph Phishing Detection

Worked on detecting Homoglyphs aka Spoof URLs & Domains; Used Image Processing & ConvNets to obtain 91% accuracy.

Voicenet

I am co-creating the Voicenet, an Open Source library which makes it easy to develop, manage and handle voice-based tasks such as Speech to Text, Gender, Age and Emotion Detection, etc. using speech samples.See More

Facebook Friend Recommendation using Graph Mining

Using a directed social graph, I predicted missing links to recommend/suggest users. The problem resembled Instagram like scenario. Used a dataset of roughly 1.86M users and performed extensive feature engineering. Later, implemented Random Forest and Randomized Search to obtain a f-1 score of 0.923 and AUC of 0.93.See More

Microsoft Malware Detection

It is a model that aims to identify whether a given piece of file/software is malware. I made us of .asm files and .byte files for every malware. Afterwhich, I performed extensive feature engineering to extract features out of these files. Later, I applied several models ranging from KNN, Logistic Regression, XGBoost with Randomized Search over both types of files individually and later combinedly.See More

Personalized Medicine-Redefining Cancer Diagnosis

It is a model that aims to detect cancer using genetic variations and evidence from text-based clinical literature. I used a wide array of approaches like Logistic Regression, Naive Bayes, KNN and Random Forests for comparisons. Later, I obtained the least misclassification percentage (37%) using Logistic Regression and Stacking various models.See More

Amazon Fashion Discovery Engine

The Amazon Fashion Discovery Engine is a Recommendation Engine to recommend similar apparel in e-commerce. I collected data of 183k products using the Product Advertising API of Amazon. I used multiple approaches like Bag of Words, tf-idf, Word2Vec, Weighted Word2Vec and Weighted Brand & Color for text-based & CNNs generated features for Image-based recommendations.See More

CNN for Text Classification

The task of text classification has typically been done with an RNN, which accepts a sequence of words as input and has a hidden state that is dependent on that sequence and acts as a kind of memory. This example shows how you can utilize convolutional layers to find patterns in sequences of word embeddings and create an effective text classifier using a CNN-based approach!See More

Quora Similar Questions

This project aims to detect whether an asked question has been asked already on Quora. I performed extensive feature engineering & generated tf-idf weighted Word2Vec features using 400k questions. Thereafter, I used Logistic Regression, Linear SVMs and XGBoost to obtain a log-loss value of 0.35.See More

Stackoverflow Tag Predictor

This project aims to suggest tags based on content that was there in the question posted on Stackoverflow. I performed feature engineering on 6M questions data. Later, I used Logistic Regression with l1 regularizer to obtain a Micro-average f-1 score of 0.44.See More

Work History

J

Senior ML Engineer

Jiffy.comDigital Assets Management Platform - one stop to search and shop 5M+ digital assets. Designed and architected the Service involving components like Metadata Generation, Indexing and Search. Used LLMs (GPT-4 Vision and Claude Models) for assets metadata generation (design, aesthetics, occasions etc.) and classification (artwork/mockups/illustrations etc.). Used ElasticSearch for Indexing. Later, Hybrid Search (Vector Search (MiniLM) and Keyword Search (BM25)) with Reranking (TinyBERT) are used for search capabilities. Available as Design Library on Jiffy.com. Jiffy Copilot - Built a conversational search assistant to assist you in shopping products you'd love. Powered by chaining multiple calls via LLMs (GPT-4-Turbo and GPT-3.5-Turbo) and Vector Search (using indexed catalog in ElasticSearch), it allows users to search and add multiple products to cart. It gathers requirements from users efficiently, asks follow-up questions and takes care of users' budget and inventory. Available to iOS users of Jiffy.
E

Senior NLP Engineer

Emplay Inc.Jan 2022 - Aug 2023 • 1 yr 8 mosConversational Search Engine: Currently integrating generative capabilities of LLMs like GPT-3 & ChatGPT into products like Search, Generation, Information Retrieval, and multi-purpose Agents for SAP and P&G via LLM Stack including LangChain. Knowledge Search Engine: Architected a scalable microservice-based system to build and search a knowledge index with over 1 million documents for P&G, supporting multiple tenants and controlled hierarchy via configs. Utilized Docker and Kubernetes for orchestration and OpenAPI standard for REST API design. Question-Answer Generation: Built a pipeline for generating high-quality question-answer pairs from indexed text documents, using T5 and Roberta models fine-tuned on the SQUAD dataset. Designed and implemented asynchronous request handling with Celery and RabbitMQ and deployed it as REST APIs using FastAPI in Docker and Kubernetes. Developed a monitoring dashboard and a testing suite for unit and stress testing with Pytest and Locust. Question-Answer Evaluation: Annotated a dataset of low and good-quality QA pairs, and adopted techniques like Weak Supervision and Active Labeling to improve annotation efficiency. Generated features attributing to question presence, grammatical question structure, and text readability. Developed a Random Forest Classifier with ~75% accuracy to filter out low-quality questions, with data and experiment tracking by DVC and MLFlow. Developed an evaluation suite to identify low-confidence samples via CleanLab and key slices via Snorkel/Sliceline. Content Moderation Engine: Developed an AI-powered content moderation engine for Simpplr, utilizing a dataset comprising ~2M comments. Trained a Mini-LM Model to detect racial, religious hate, insulting, and explicit comments with ~91% accuracy. Utilized ONNX Conversion and Dynamic Quantization to optimize the solution for speed and storage space. Served in production with FastAPI and Docker and actively used by 500+ companies.
A

Machine Learning Engineer

Albot TechnologiesSep 2020 - Jan 2022 • 1 yr 5 mosLed the build of SafeNet: Under the aegis of UNICEF Innovation Fund, an AI-powered extension meant to detect & handle visual, toxic content prevalent over the web in order to safeguard the internet browsing experience of children. Worked on building Cyber Anomaly Detection System: an ML-based tool to automatically detect network anomalies and 14 different types of attacks such as DoS, DDoS, Heartbleed, and others in real-time. Worked on building Homoglyphs Phishing Detection System: an ML-based tool to automatically detect visually similar phishing domains via CNNs and to safeguard user information. Worked on building Email Phishing Detection: an ML-based tool to analyze various properties of raw email files and predict whether incoming emails are phishing emails or not in real-time.
R

Machine Learning Engineer

RobofiedAug 2019 - Sep 2020 • 1 yr 2 mosWorking on Voicenet-a comprehensive python library for performing speech/voice based functions ranging from Speech to Text, Gender;Age and Emotion detection from Voice and many more! Creating Content in as technical write-ups, blogs, case-studies and mini projects and efficient models by using bleeding-edge state of the art techniques and tools.
R

Machine Learning Intern

RobofiedJun 2019 - Jul 2019 • 2 mosWorked on Identification of the Gender of the Speaker based on Voice Samples with commendable accuracy in order to provide human alike behavior by Model.
A

Content Developer

Aegisisc Software Solutions Pvt LtdMar 2020 - Jun 2020 • 4 mosWorking on creating quality technical write-ups and blogs on the topics ranging from intricacies in traditional Machine Learning to state-of-the-art Models in Computer Vision and Natural Language Processing.

Education

D

Dr. A.P.J. Abdul Kalam Technical University

Bachelor of Technology, Computer Science and Engineering2016 - 2020

How Pangea Works

Effortlessly discover top talent

We've distilled the candidate search from endless hours down to just a few minutes. Using Pangea's AI-powered search tools, you can find top fractional talent able to take on your next project. Our system looks at your company's niche and your needs to find the perfect match faster than any traditional hiring platform.

Start working with talent today

The top talent on Pangea is ready to get started with you right now. You can message or hire a candidate right from their profile page and start assigning work as soon as they respond. And the best part? Pangea's fractional contract structure lets you start small and ramp up as your needs change, keeping your costs manageable and your team's capabliities flexible.

Track work and invoices in one place

Assign tasks, track progress, and complete invoices all on Pangea. We've combined every part of the hiring process into one platform to eliminate the miscommunication that's unavoidable on other freelance platforms. We even send out 1099s to your contractors at the end of the year!

Talk with a Talent Expert

Members of our team are available to help you speed through the hiring process.
Available Now
Book a Call
Machine Learning Engineer | NLP Specialist
With a Bachelor of Technology in Computer Science and Engineering from Dr. A.P.J. Abdul Kalam Technical University, this professional has a strong educational foundation. Their experience includes roles such as Senior ML Engineer at Jiffy.com, Senior NLP Engineer at Emplay Inc., and Machine Learning Engineer at Albot Technologies, where they led the development of various AI-powered tools and systems. They also contributed to projects at Robofied and Aegisisc Software Solutions Pvt Ltd, showcasing their expertise in speech/voice-based functions, content development, and technical writing. Their work involves integrating generative capabilities of LLMs like GPT-3 & ChatGPT, building conversational search experiences, and developing AI-powered content moderation engines. With a focus on machine learning, natural language processing, and computer vision, they have consistently demonstrated their ability to innovate and deliver impactful solutions.

Talk with a Talent Expert

Members of our team are available to help you speed through the hiring process.
Available Now
Book a Call

Top Talent like Laxman are on Pangea

Pangea, a YC company, connects companies with fractional talent. Fractional hiring allows companies to move faster and work with more specialized talent, while giving talent more flexibility and independence. If you are talent open to fractional work, apply here. If you’re a company looking for high-quality fractional talent, learn more here.

Laxman Tomar

AI & Machine Learning Engineer
Gurugram, HR, IN
Deep Learning
Project Development
Software Development
Natural Language Processing
Machine Learning
Available for hire fromNegotiable
Contracts

Projects

Jiffy Copilot

AI Assistant powered by LLMs for shopping products.

Conversational Search Engine

Architected and Built next-gen conversational search products for SAP, P&G and Equinix. Powered by LLMs like GPT-3.5, Chat-GPT and Falcon via LLM Stack including LangChain. Used Retrieval Augmented Generation to answer customer-specific queries using their data. Operates on top of indexed data into Elasticsearch (updates automatically as new data is added) and Web in real-time. Using Websockets and Async technology paired up with streaming responses to provide a seamless search experience. Holds conversational context to answer follow-up search questions using Query Rephrase and Vector Memory Approach. Powered by Prompting and other approaches to reduce Model Hallucinations and PII Issues. Supports multiple tenants through customer-specific configurations. Actively being used in production by SAP, P&G and Equinix etc.

Knowledge Search Engine

Architected the System to build composable Microservices for Indexing, Generation, and Search Components. Supports multiple tenants due to hierarchy controlled by API Parameters, Customer Configs, and Environments. All the Enrichment Servies are developed as a Microservice by Docker and orchestrated by Kubernetes. APIs are designed as per OpenAPI Standard to develop REST endpoints for all the services. Logging and Monitoring Services are integrated into each enrichment service for refinement purposes. Currently in Production and scales to ingest, generate and search over 1M+ documents.

Question Answer Generation

Generate Question Answer pairs from indexed text documents. Used T5 fine-tuned on SQUAD as Generator to generate questions from the text of ingested documents. Used Roberta fine-tuned on SQUAD as Reader to return answers with scores for generated questions. Ingested generated QA pairs to ElasticSearch Index. Developed Asynchronous Request Handling Mechanism using Celery and RabbitMQ. Developed RESTAPIs using FastAPI served with Docker & deployed via Kubernetes. Developed a monitoring Dashboard via Flower to track requests getting processed in real time. Built Testing Suite for Unit and Stress Testing via Pytest and Locust.

Question Answer Evaluation

Evaluate generated Question Answer pairs to filter out low-quality questions. Built & annotated a dataset of low and good-quality QA pairs with Argilla. Adopted techniques like Weak Supervision and Active Labeling to annotate more samples quickly. Generated features attributing to Question Presence, Grammatical Question Structure, and Text Readability. Built a Random Forest Classifier to obtain ~75% accuracy with data & experiment tracking by DVC + MLFlow. Developed Evaluation Suite to identify low-confidence samples via CleanLab & key slices via Snorkel/Sliceline. Performed Interpretability Analysis via SHAP to identify features influencing misclassifications. Evaluation Strategies helped improve the accuracy to ~85%.

Content Moderation Engine

AI-powered Content Moderation Engine for Simpplr. Used Jigsaw's Unintended Bias in Toxicity Classification Challenge Dataset comprising ~2M comments. Trained a Mini-LM Model to detect racial, religious hate, insulting, and explicit comments with ~91% accuracy. Developed a Dynamic Thresholding Mechanism, Emojis Decoder, and PII Handler to make the solution robust. Used ONNX Conversion & Dynamic Quantization to make the solution 4x faster with 1/4th less storage space. Served by FastAPI and Docker in production and actively being used by 500+ companies.

Email Phishing Detection

Worked on detecting whether an Email in Mailbox is Normal, Spam or Phishing; Performed Extensive Featurization alongwith Transformers based Embeddings to obtain 5% error rate.

Network Anaomly Detection

Worked on detecting 15 Types of Network Attacks including DOS, DDOS, Bot, SQL Injection, directly from the Network Interface. Obtained 94% accuracy using Stacked Model.

Homoglyph Phishing Detection

Worked on detecting Homoglyphs aka Spoof URLs & Domains; Used Image Processing & ConvNets to obtain 91% accuracy.

Voicenet

I am co-creating the Voicenet, an Open Source library which makes it easy to develop, manage and handle voice-based tasks such as Speech to Text, Gender, Age and Emotion Detection, etc. using speech samples.

Facebook Friend Recommendation using Graph Mining

Using a directed social graph, I predicted missing links to recommend/suggest users. The problem resembled Instagram like scenario. Used a dataset of roughly 1.86M users and performed extensive feature engineering. Later, implemented Random Forest and Randomized Search to obtain a f-1 score of 0.923 and AUC of 0.93.

Microsoft Malware Detection

It is a model that aims to identify whether a given piece of file/software is malware. I made us of .asm files and .byte files for every malware. Afterwhich, I performed extensive feature engineering to extract features out of these files. Later, I applied several models ranging from KNN, Logistic Regression, XGBoost with Randomized Search over both types of files individually and later combinedly.

Personalized Medicine-Redefining Cancer Diagnosis

It is a model that aims to detect cancer using genetic variations and evidence from text-based clinical literature. I used a wide array of approaches like Logistic Regression, Naive Bayes, KNN and Random Forests for comparisons. Later, I obtained the least misclassification percentage (37%) using Logistic Regression and Stacking various models.

Amazon Fashion Discovery Engine

The Amazon Fashion Discovery Engine is a Recommendation Engine to recommend similar apparel in e-commerce. I collected data of 183k products using the Product Advertising API of Amazon. I used multiple approaches like Bag of Words, tf-idf, Word2Vec, Weighted Word2Vec and Weighted Brand & Color for text-based & CNNs generated features for Image-based recommendations.

CNN for Text Classification

The task of text classification has typically been done with an RNN, which accepts a sequence of words as input and has a hidden state that is dependent on that sequence and acts as a kind of memory. This example shows how you can utilize convolutional layers to find patterns in sequences of word embeddings and create an effective text classifier using a CNN-based approach!

Quora Similar Questions

This project aims to detect whether an asked question has been asked already on Quora. I performed extensive feature engineering & generated tf-idf weighted Word2Vec features using 400k questions. Thereafter, I used Logistic Regression, Linear SVMs and XGBoost to obtain a log-loss value of 0.35.

Stackoverflow Tag Predictor

This project aims to suggest tags based on content that was there in the question posted on Stackoverflow. I performed feature engineering on 6M questions data. Later, I used Logistic Regression with l1 regularizer to obtain a Micro-average f-1 score of 0.44.

Work History

J

Senior ML Engineer

Jiffy.comDigital Assets Management Platform - one stop to search and shop 5M+ digital assets. Designed and architected the Service involving components like Metadata Generation, Indexing and Search. Used LLMs (GPT-4 Vision and Claude Models) for assets metadata generation (design, aesthetics, occasions etc.) and classification (artwork/mockups/illustrations etc.). Used ElasticSearch for Indexing. Later, Hybrid Search (Vector Search (MiniLM) and Keyword Search (BM25)) with Reranking (TinyBERT) are used for search capabilities. Available as Design Library on Jiffy.com. Jiffy Copilot - Built a conversational search assistant to assist you in shopping products you'd love. Powered by chaining multiple calls via LLMs (GPT-4-Turbo and GPT-3.5-Turbo) and Vector Search (using indexed catalog in ElasticSearch), it allows users to search and add multiple products to cart. It gathers requirements from users efficiently, asks follow-up questions and takes care of users' budget and inventory. Available to iOS users of Jiffy.
E

Senior NLP Engineer

Emplay Inc.Jan 2022 - Aug 2023 • 1 yr 8 mosConversational Search Engine: Currently integrating generative capabilities of LLMs like GPT-3 & ChatGPT into products like Search, Generation, Information Retrieval, and multi-purpose Agents for SAP and P&G via LLM Stack including LangChain. Knowledge Search Engine: Architected a scalable microservice-based system to build and search a knowledge index with over 1 million documents for P&G, supporting multiple tenants and controlled hierarchy via configs. Utilized Docker and Kubernetes for orchestration and OpenAPI standard for REST API design. Question-Answer Generation: Built a pipeline for generating high-quality question-answer pairs from indexed text documents, using T5 and Roberta models fine-tuned on the SQUAD dataset. Designed and implemented asynchronous request handling with Celery and RabbitMQ and deployed it as REST APIs using FastAPI in Docker and Kubernetes. Developed a monitoring dashboard and a testing suite for unit and stress testing with Pytest and Locust. Question-Answer Evaluation: Annotated a dataset of low and good-quality QA pairs, and adopted techniques like Weak Supervision and Active Labeling to improve annotation efficiency. Generated features attributing to question presence, grammatical question structure, and text readability. Developed a Random Forest Classifier with ~75% accuracy to filter out low-quality questions, with data and experiment tracking by DVC and MLFlow. Developed an evaluation suite to identify low-confidence samples via CleanLab and key slices via Snorkel/Sliceline. Content Moderation Engine: Developed an AI-powered content moderation engine for Simpplr, utilizing a dataset comprising ~2M comments. Trained a Mini-LM Model to detect racial, religious hate, insulting, and explicit comments with ~91% accuracy. Utilized ONNX Conversion and Dynamic Quantization to optimize the solution for speed and storage space. Served in production with FastAPI and Docker and actively used by 500+ companies.
A

Machine Learning Engineer

Albot TechnologiesSep 2020 - Jan 2022 • 1 yr 5 mosLed the build of SafeNet: Under the aegis of UNICEF Innovation Fund, an AI-powered extension meant to detect & handle visual, toxic content prevalent over the web in order to safeguard the internet browsing experience of children. Worked on building Cyber Anomaly Detection System: an ML-based tool to automatically detect network anomalies and 14 different types of attacks such as DoS, DDoS, Heartbleed, and others in real-time. Worked on building Homoglyphs Phishing Detection System: an ML-based tool to automatically detect visually similar phishing domains via CNNs and to safeguard user information. Worked on building Email Phishing Detection: an ML-based tool to analyze various properties of raw email files and predict whether incoming emails are phishing emails or not in real-time.
R

Machine Learning Engineer

RobofiedAug 2019 - Sep 2020 • 1 yr 2 mosWorking on Voicenet-a comprehensive python library for performing speech/voice based functions ranging from Speech to Text, Gender;Age and Emotion detection from Voice and many more! Creating Content in as technical write-ups, blogs, case-studies and mini projects and efficient models by using bleeding-edge state of the art techniques and tools.
R

Machine Learning Intern

RobofiedJun 2019 - Jul 2019 • 2 mosWorked on Identification of the Gender of the Speaker based on Voice Samples with commendable accuracy in order to provide human alike behavior by Model.
A

Content Developer

Aegisisc Software Solutions Pvt LtdMar 2020 - Jun 2020 • 4 mosWorking on creating quality technical write-ups and blogs on the topics ranging from intricacies in traditional Machine Learning to state-of-the-art Models in Computer Vision and Natural Language Processing.

Education

D

Dr. A.P.J. Abdul Kalam Technical University

Bachelor of Technology, Computer Science and Engineering2016 - 2020

How Pangea Works

Effortlessly discover top talent

We've distilled the candidate search from endless hours down to just a few minutes. Using Pangea's AI-powered search tools, you can find top fractional talent able to take on your next project. Our system looks at your company's niche and your needs to find the perfect match faster than any traditional hiring platform.

Start working with talent today

The top talent on Pangea is ready to get started with you right now. You can message or hire a candidate right from their profile page and start assigning work as soon as they respond. And the best part? Pangea's fractional contract structure lets you start small and ramp up as your needs change, keeping your costs manageable and your team's capabliities flexible.

Track work and invoices in one place

Assign tasks, track progress, and complete invoices all on Pangea. We've combined every part of the hiring process into one platform to eliminate the miscommunication that's unavoidable on other freelance platforms. We even send out 1099s to your contractors at the end of the year!

Talk with a Talent Expert

Members of our team are available to help you speed through the hiring process.
Available Now
Book a Call
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