Key Responsibilities :

- Work with Python, LLM/GenAI frameworks and tools AI/ML end-to-end solutions developing

- CI/CD pipelines development, LLM model containerizing and deployment on cloud or premise.

- Models testing and follow-up maintenance.

- All stages of ML model life cycle ensuring and support.

- Design prototypes and POCs to demonstrate solution feasibility and value.

- Provide architecture solution.

- Research, design, build, and train innovative applications of LLMs to solve complex real-world problems.

- Provide technical guidance to clients adopting LLM technologies.

Required Qualifications :

- Bachelor's Degree (might be final course student) in Statistics, Applied Mathematics, Computer Science, or other related fields

- 3+ years of hands-on Python development experience; 2+ years building and maintaining production APIs (FastAPI, Flask); strong software engineering fundamentals including testing, version control, and code quality

- 2+ years with AI/ML/NLP/LLM technologies; 1+ year production experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI); hands-on experience with LLM frameworks (LangChain or similar); proficiency in prompt engineering, RAG implementation, and vector databases

- 2+ years with cloud platforms (AWS, GCP, or Azure); 2+ years of experience with LLMOps/MLOps, model evaluation, CI/CD pipeline development, containerization, model deployment in test and production environments, familiarity with LLM observability tools

Desired Qualifications :

- M.Sc. or Ph. in Computer Science, AI/ML, or related field

- Experience with fine-tuning techniques (LoRA, QLoRA, PEFT), quantization, and model optimization; hands-on with open-source models (Llama, Phi, Gemma) and frameworks (HuggingFace Transformers, vLLM)

- Proficiency with additional languages (Bash, TypeScript); experience with data platforms (Databricks, Snowflake); knowledge of graph databases (Neo4j) for advanced RAG

- Expertise in agentic frameworks (LangGraph, AutoGen, Semantic Kernel), multi-agent systems, tool use/function calling, and complex orchestration patterns

- Strong understanding of Responsible AI, including bias detection, guardrails implementation, content safety, data privacy, and AI governance; applied research experience in NLP/LLM domains