AI/ML Engineer

We are into staff Augg services since last 3 decades. I am reaching out to see if you are looking for New Opportunity.At the same time , for my enterprise client , they need genuine candidate with absolute zero manipulation of candidate's document. Even if you are having 2 years of latest exp in AI stacks is absolute fine , lets respect each other time and share original documents

Required Skills
•Machine Learning & AI foundations
• Strong grounding in ML fundamentals — supervised/unsupervised learning, evaluation methodology, and model selection.
• Practical experience with deep learning frameworks (PyTorch and/or TensorFlow).
• Solid understanding of NLP and transformer architectures.

Generative AI, LLMs & Agentic Systems
• Hands on experience building with LLMs (OpenAI/Azure OpenAI, Anthropic Claude, or comparable).
• Prompt engineering, structured outputs, and function/tool calling.
• Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or equivalent) and multi agent design patterns.
• RAG system design: vector databases, embeddings, retrieval and re ranking strategies, and grounding/citation techniques.
• Familiarity with the Model Context Protocol (MCP) or similar tool/integration standards.

Data Engineering
• Strong SQL and experience with relational databases (SQL Server, PostgreSQL, or similar).
• Building and maintaining data/ML pipelines and workflow orchestration (Airflow or equivalent).
• Comfort working with unstructured and semi structured data at scale.

MLOps & Observability
• Model/LLM evaluation frameworks and offline/online testing.
• Observability tooling for AI systems (e.g., Arize, Langfuse, or comparable) — monitoring quality, cost, drift, and token usage.
• Experiment tracking and reproducibility practices.
Software Engineering & Cloud
• Expert level Python and sound software engineering habits (testing, code review, version control with Git/GitHub).
• Containerization with Docker and CI/CD (GitHub Actions or equivalent).
• Cloud platform experience (Azure preferred; AWS/Google Cloud Platform acceptable), including deploying and scaling services.