VAIBHAV SHELAR
@vaibhavshelar
Machine Learning Engineer building production AI systems—LLMs, RAG, and agents—with deep post-training focus.
IndiaWhat I'm looking for
I’m a Machine Learning Engineer focused on the training side—turning research-grade ideas into production AI systems. I’ve designed agentic AI systems for complex reasoning and multi-step execution using LangChain, CrewAI, and Agno, built scalable RAG pipelines with vector search and semantic embeddings, and led end-to-end deployment across AWS and GCP using serverless architectures (Lambda, API Gateway, Step Functions) and managed AI platforms like Bedrock, Vertex AI, and SageMaker.
I also optimize inference for real-world constraints by applying quantization and containerized deployments with Docker, then fine-tune and align multimodal models. I’ve fine-tuned Qwen2-VL-2B vision-language models with MS Swift and PEFT, built RLHF pipelines, and worked on iterative post-training loops for tasks like extracting text from handwritten documents—validating that each new checkpoint truly improves performance. I enjoy fixing “messy” live systems—reducing latency, handling edge cases, and improving reliability—without rebuilding everything from scratch.
Experience
Work history, roles, and key accomplishments
• Designed and built agentic AI systems for complex reasoning and multi-step task execution using LangChain, CrewAI, and Agno, improving automation across workflow-driven use cases.
• Developed scalable RAG pipelines with vector search and semantic embeddings, improving accuracy and relevance in document retrieval and knowledge extraction.
• Led end-to-end deployment of ML systems across AWS and G
• Worked on agentic AI systems, contributing to the training, evaluation, and optimization of autonomous multi-step AI agents powered by Large Language Models (LLMs) and reinforcement learning-based feedback mechanisms.
• Designed and refined prompts, tool-calling workflows, and reasoning pipelines to improve agent decision-making, task execution accuracy, safety, and reliability across real-world
• Built data processing pipelines for financial analysis, automating ETL workflows and reducing manual effort by 60% while improving data reliability and operational efficiency.
• Developed Python-based data pipelines to process large-scale financial datasets, enabling predictive analytics and data-driven modeling.
• Implemented automated data extraction using Playwright and APIs, parsing JSON res
• Contributed to the development of a financial data portal, working across backend services, data pipelines, and deployment layers to deliver scalable end-to-end functionality.
• Built and optimized data processing pipelines using Python and Pandas, enabling efficient transformation and handling of large-scale financial datasets for downstream applications.
• Designed and implemented automated re
Education
Degrees, certifications, and relevant coursework
NIIT University
Bachelor of Technology - BTech, Computer Engineering
2019 - 2023
Tech stack
Software and tools used professionally
Availability
Location
IndiaAuthorized to work in
Job categories
Skills
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