Senior Data Scientist – GenAI

We are looking for an experienced professional to join projects focused on Generative AI, LLMs, RAG, and AI Agents . The rol
Requirements:
6+ years of experience in Data Science, AI Engineering, Machine Learning, or a related field. 4+ years of professional Python development experience. 2+ years of hands-on experience with LLMs / Generative AI , preferably with RAG-based solutions. Strong knowledge of Python, LLMs, RAG, and Generative AI . Practical experience with LangChain, LlamaIndex, LangGraph , or similar frameworks. Experience with AI Agents / Agentic AI ; MCP experience is a strong advantage. Knowledge of vector databases, hybrid search, reranking, and LLM evaluation . Experience with model fine-tuning , such as LoRA, QLoRA, or SFT. Experience with at least one major cloud platform: Azure, AWS, or GCP . Familiarity with Docker, Kubernetes, and CI/CD . Good understanding of AI/ML architectures and production deployment practices . Excellent English communication skills . Nice to Have Experience with Spark / Databricks . Experience with GitHub Actions, GitLab CI, or Azure DevOps . Experience with multi-agent systems and agentic AI workflows . Experience building scalable, production-grade AI systems. Experience with multimodal AI models .
Must have: Data science, AI, Machine learning, Python, Cloud platform, AWS, Docker, Kubernetes
Nice to have: Spark, Databricks, GitHub, GitLab CI, Azure DevOps
Daily tasks: - Design and develop production-grade solutions using LLMs and Generative AI.
– Build and optimize RAG systems, including vector search, hybrid search, reranking, and query optimization.
– Develop AI Agents and agentic AI solutions.
– Work with frameworks such as LangChain, LlamaIndex, LangGraph, or similar technologies.
– Work with MCP to integrate AI agents with external tools and systems.
– Select appropriate models and develop prompt engineering and fine-tuning strategies, including LoRA/QLoRA/SFT.
– Implement LLM evaluation, validation, guardrails, and quality control mechanisms.
– Develop APIs/backend components and integrate AI solutions with existing systems.
– Contribute to technical architecture and technology selection.
– Collaborate with engineers, developers, and product teams.
– Share knowledge and support less experienced team members.