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Hey there! I'm Henil and I'm a
Machine Learning Engineer from Boston, MA.

While I am coding since 18 years of age, I've learned more from breaking things in production than from any tutorial. I build AI that actually works in the real world and solve real problems.

Seeking full-time ML roles
3+
Years of
Industry Experience
6+
Years of
Python, SQL, Cloud
5+
Years of
ML, DL, NLP, CV
2+
Years of
LLMs, Agents, RAG
7+
Publications in
MLSys, IEEE, Elsevier
6+
Years of
Python, SQL, Cloud
5+
Years of
ML, DL, NLP, CV
3+
Years of
Industry Experience
10+
End-to-End
ML Projects Owned
7+
Publications in
MLSys, IEEE, Elsevier
2+
Years of
LLMs, Agents, RAG
6+
Years of
Python, SQL, Cloud
3+
Years of
Industry Experience
5+
Years of
ML, DL, NLP, CV
2+
Years of
LLMs, Agents, RAG
10+
End-to-End
Projects Owned
7+
Publications in
MLSys, IEEE, Elsevier

About me

Personal Image
Personal Image

My journey into ML started in an unexpected place, I was preparing an autonomous car for an electric vehicle competition during my undergrad in Electronics and Communication Engineering. As team captain, I dove headfirst into computer vision and machine learning, and honestly, I was hooked. That curiosity led me to join Hyperlab as a Founding ML Engineer, where we built an ML-driven sports analytics app that got us featured on Shark Tank India - easily one of the proudest moments of my life. But more than the spotlight, Hyperlab taught me how to ship products people actually use, close deals, optimize under constraints, and build fast.

I moved to the US to deepen my expertise in Data Science at Northeastern, and the GenAI boom happened. Since then, I've been laser-focused on agentic AI, RAG, LLM fine-tuning, and production systems. Through three co-ops at Waters Corporation, Edlight, and ImpacterAI, I've built chatbots, optimized inference, and automated LLM evaluations. I've learned that great ML engineering isn't just about knowing the frameworks - it's about making smart tradeoffs, asking hard questions, and building systems that scale under real-world pressure.

Now wrapping up my MS in Data Science (4.0 GPA, Dec 2025), I'm actively seeking full-time ML Engineer roles starting January 2026 where I can continue building production AI that makes an impact.

  • Production ML
    Agentic AI
    RAG
    LLM Fine-Tuning
    Distributed Training
    Deep Learning
    Model Evaluations
    Cloud Architecture
    Inference Optimization
    CI/CD/CT/CM
  • Production ML
    Agentic AI
    RAG
    LLM Fine-Tuning
    Distributed Training
    Deep Learning
    Model Evaluations
    Cloud Architecture
    Inference Optimization
    CI/CD/CT/CM
  • Production ML
    Agentic AI
    RAG
    LLM Fine-Tuning
    Distributed Training
    Deep Learning
    Model Evaluations
    Cloud Architecture
    Inference Optimization
    CI/CD/CT/CM

My Toolbox

Languages

Python, SQL, Git, Bash, CUDA, Swift, TypeScript, C++, HTML/CSS

Languages

Python, SQL, Git, Bash, CUDA, Swift, TypeScript, C++, HTML/CSS

Languages

Python, SQL, Git, Bash, CUDA, Swift, TypeScript, C++, HTML/CSS

Frameworks

Deep Learning - PyTorch, TensorFlow, Keras, JAX

Generative AI - Transformers, LangGraph, LangChain, CrewAI, Hugging Face

Traditional ML - Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn, NLTK

Computer Vision - OpenCV, MediaPipe, Albumentations, YOLO

Frameworks

Deep Learning - PyTorch, TensorFlow, Keras, JAX

Generative AI - Transformers, LangGraph, LangChain, CrewAI, Hugging Face

Traditional ML - Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn, NLTK

Computer Vision - OpenCV, MediaPipe, Albumentations, YOLO

Frameworks

Deep Learning - PyTorch, TensorFlow, Keras, JAX

Generative AI - Transformers, LangGraph, LangChain, CrewAI, Hugging Face

Traditional ML - Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn, NLTK

Computer Vision - OpenCV, MediaPipe, Albumentations, YOLO

Train, Eval & Infer

Training - LoRA, QLoRA, DeepSpeed Zero, Ray, Pytorch FSDP

Evaluation - MLFlow, WandB, DeepEval, RAGAS

Inference - vLLM, MLX, Diffusers, Ollama, Groq, Baseten

Train, Eval & Infer

Training - LoRA, QLoRA, DeepSpeed Zero, Ray, Pytorch FSDP

Evaluation - MLFlow, WandB, DeepEval, RAGAS

Inference - vLLM, MLX, Diffusers, Ollama, Groq, Baseten

Train, Eval & Infer

Training - LoRA, QLoRA, DeepSpeed Zero, Ray, Pytorch FSDP

Evaluation - MLFlow, WandB, DeepEval, RAGAS

Inference - vLLM, MLX, Diffusers, Ollama, Groq, Baseten

Databases

Big Data - Apache Spark, Hadoop, Hive, Databricks

RDBMS/NoSQL - PostreSQL, MySQL, MongoDB, Firebase, Redis

VectorDB - ChromaDB, Pinecone, Weaviate, Datastax AstraDB

Databases

Big Data - Apache Spark, Hadoop, Hive, Databricks

RDBMS/NoSQL - PostreSQL, MySQL, MongoDB, Firebase, Redis

VectorDB - ChromaDB, Pinecone, Weaviate, Datastax AstraDB

Databases

Big Data - Apache Spark, Hadoop, Hive, Databricks

RDBMS/NoSQL - PostreSQL, MySQL, MongoDB, Firebase, Redis

VectorDB - ChromaDB, Pinecone, Weaviate, Datastax AstraDB

Cloud

AWS - Lambda, EC2, S3, Sagemaker, DynamoDB, OpenSearch, Bedrock, Glue

GCP - Cloud Run, Cloud Storage, Firebase, Cloud SQL, VertexAI, BigQuery

Azure - AzureML, Azure OpenAI, Azure Kubernetes Service, CosmosDB

Cloud

AWS - Lambda, EC2, S3, Sagemaker, DynamoDB, OpenSearch, Bedrock, Glue

GCP - Cloud Run, Cloud Storage, Firebase, Cloud SQL, VertexAI, BigQuery

Azure - AzureML, Azure OpenAI, Azure Kubernetes Service, CosmosDB

Cloud

AWS - Lambda, EC2, S3, Sagemaker, DynamoDB, OpenSearch, Bedrock, Glue

GCP - Cloud Run, Cloud Storage, Firebase, Cloud SQL, VertexAI, BigQuery

Azure - AzureML, Azure OpenAI, Azure Kubernetes Service, CosmosDB

Deploy & Web

Deploy - Docker, Github Actions, Kubernetes, CI/CD

Monitor - LangFuse, LangSmith, Datadog, AWS CloudWatch

Web - Streamlit, Flask, Django, FastAPI

Deploy & Web

Deploy - Docker, Github Actions, Kubernetes, CI/CD

Monitor - LangFuse, LangSmith, Datadog, AWS CloudWatch

Web - Streamlit, Flask, Django, FastAPI

Deploy & Web

Deploy - Docker, Github Actions, Kubernetes, CI/CD

Monitor - LangFuse, LangSmith, Datadog, AWS CloudWatch

Web - Streamlit, Flask, Django, FastAPI

The APPS that Are a part of My Daily routine
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The APPS that Are a part of My Daily routine
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