- 4+ years applying computational algorithms and statistical methods to structured and unstructured data.

- 3+ years with AI Cloud services (e.g., Azure ML, Databricks, Azure OpenAI), or equivalent on GCP/AWS.

- 1+ year hands-on with LLMs, RAG (Retrieval-Augmented Generation), and Agentic AI frameworks.

- Significant experience deploying scalable solutions end-to-endfrom problem definition to successful product launch.

- Practical experience in model performance tuning (latency, throughput, accuracy) including quantization and optimization.

- Strong Python development; expertise in machine learning and deep learning with TensorFlow or PyTorch (including distributed training).

- Deep subject-matter expertise in text processing, NLU, and SLU.

- Proven ability to design effective prompts and optimize them for task performance; experience applying ICL.

- Data engineering fundamentals : ingestion, transformation, and management.

- Mandatory : Hands-on experience with Agentic AI frameworks such as Microsoft AutoGen, LangChain, LangGraph, and Semantic Kernel.

- Excellent communication, writing, and presentation skills.

- Ability to decompose complex problems into clear, implementable solutions and lead cross-functional delivery.

Tools & Technologies :

- Programming & DL : Python, TensorFlow, PyTorch, Hugging Face Transformers

- LLM Observability & Evaluation : Langfuse, LangSmith

- LLM & Orchestration : Azure OpenAI, LangChain, LangGraph, Semantic Kernel, Microsoft AutoGen

- RAG & Vector DBs : Azure AI Search, FAISS, Pinecone, Weaviate

- Cloud & Data : Azure ML, Databricks, Azure AI Foundry, Azure Data Factory (plus equivalents on GCP/AWS)

- Infra & Ops : Docker, Kubernetes, CI/CD, Terraform (preferred)

- Agentic AI frameworks : ADK, Autogen, LangChain, LangGraph