Developing complex algorithms using LLM, ML, DL (CNN, RNN, LSTM, Bi-LSTM, Deep RNN, GAN, RL), and Computer Vision.

Integrating SageMaker with AWS data services (S3, Glue, Athena, Redshift) for seamless data ingestion and preparation.

Building end-to-end MLOps workflows using MLflow, Kubeflow, and SageMaker pipelines.

Integrating PyTorch models into production environments using TorchServe, ONNX, and FastAPI endpoints.

Deploying RAG solutions via FastAPI microservices and containerized workloads on AWS, Azure, and GCP.

Integrating Agentic AI agents with MLOps workflows and designing and implementing GPT-based AI solutions (ChatGPT, Copilot, Bedrock).

Designing NLP pipelines using NLTK and implementing ML models on AWS EC2 and GCP Vertex AI.

Optimizing and fine-tuning LLM models and integrating FastAPI services with MLOps tools (MLflow, Kubeflow) for model lifecycle management.

Using Python (NumPy, SciPy, Pandas, Scikit-learn, Matplotlib, Seaborn) and PySpark/MLlib for model development.

Conducting cost optimization in MLOps pipelines and automating ML workflows using Vertex AI Playbooks.

Designing and deploying ML models into production environments, ensuring scalability and performance.

Developing and implementing RAG pipelines and Agentic AI workflows using LLM fine-tuning and prompt engineering.

Deploying AI/ML workloads on GKE and orchestrating ML pipelines using GKE + Kubernetes.

Built a document recommendation engine using multiple feature sets to improve client document retrieval.

Proposed and deployed GPU-based AI/DL solutions for cognitive computing projects.

Implemented RAG systems using LangChain, LlamaIndex, and Hugging Face Transformers for multi-source knowledge integration.

Developed API documentation and interactive testing using FastAPI’s built-in OpenAPI/Swagger UI support.

Developed Flask APIs to expose ML/DL functionalities for cross-application integration.

Designed and implemented RAG pipelines to enhance LLM responses and applied metadata filtering and namespace partitioning in Vector DBs.

Implemented distributed training on SageMaker using GPU/CPU clusters for large dataset processing.

Integrated FastAPI with Vector databases (Pinecone, FAISS, Milvus) and created PyTorch datasets and data loaders to efficiently handle large, multi-format datasets with real-time augmentation.

Created and optimized chatbots for client query resolution and collaborated with cross-functional teams to embed GenAI into SaaS products.

Designed automated data pipelines with Apache Beam, NiFi, and RDF Graphs on GCP Dataflow & BigQuery.

Optimized PyTorch models and built end-to-end MLOps workflows using MLflow, Kubeflow, and SageMaker pipelines.

Developed scalable RAG services and built regression models (Lasso, Ridge, SVR, XGBoost).

Built Agentic AI systems and Vector DB integrations within LangChain and LlamaIndex workflows for AI agents and knowledge bots.

Architected FastAPI services to expose AI/ML inference pipelines and optimized GenAI inference performance.

Built an NLP-driven HR case management system and integrated ChatGPT into customer service, reducing response time by 20%.

Built IaC templates for MLOps environments using Terraform and AWS CloudFormation.

Developed scalable data pipelines using Spark (Core, SQL, Streaming, MLlib) with Python and Scala.

Designed and deployed AI solutions across domains, including predictive analytics, anomaly detection, recommender systems, and vehicle pricing models.

Built ML models using Scikit-learn and PyTorch and deployed them with Docker, Kubernetes, and AWS SageMaker.

Worked on performance tuning, CI/CD, and cloud platforms (AWS, GCP).

Used C++ for system-level tasks including socket programming, embedded automation, and SOAP client development.

Designed and deployed data pipelines (EtLT) and ML workflows using LSTM, NLP, Kubeflow, AWS SageMaker, Greengrass, and GCP services.

Fine-tuned LLMs within RAG architectures, implemented GenAI content moderation, and developed and optimized Agentic AI agents with LLMs.

Developed SageMaker training pipelines, designed FastAPI endpoints for real-time prediction services, and deployed GenAI applications as scalable APIs using FastAPI, Docker, and Kubernetes for high-availability services.

Integrated RAG workflows with vector databases (Pinecone, FAISS, Milvus) for semantic search and context enrichment.

Applied NLP to enhance customer satisfaction and evaluated models using F-Score, ROC-AUC, MAE, RMSE.

Built predictive models (KNN, Deep Learning with PyTorch) and developed image classification systems using OpenCV.

Extracted, cleaned, and analyzed large datasets using Python, Pandas, NumPy, and advanced Excel to build reports and support business decisions.

Optimized embedding storage and retrieval in Vector DBs to improve search latency and query accuracy.

Monitored PyTorch model performance using TensorBoard and Weights & Biases for experiment tracking.

Designed and implemented Agentic AI workflows and used PyTorch to train an ML model.

Analyzed product data on the market and chose which products to sell and which to stop.

Acquired data from primary or secondary data sources and maintained databases/data systems.

Used advanced Excel functions to generate spreadsheets and pivot tables.

Led end-to-end test efforts—planning, execution, and reporting—across releases.

Developed test automation frameworks using Python and SeeTest APIs and created a Requirement Traceability Tool.

Collaborated with developers for issue reproduction and participated in code/test case reviews.

Proactively prepared risk mitigation plans and trained new joiners in automation and test reporting.

Collaborated with cross-functional teams to integrate PyTorch AI models into enterprise-grade AI/ML systems.

Tested entire front-end and back-end modules using Python on Django Web Framework.

Implemented the application using Python Spring IoC and Django and handled security using Python Spring Security.