- 7 - 12 years, including demonstrable hands-on experience shipping GenAI/agentic applications to production.
- Design and build end-to-end GenAI applications: RAG systems, agentic workflows, tool-using assistants, and multi-agent orchestration.
- Own architecture decisions across model selection, retrieval design, orchestration, and serving balancing cost, latency, and quality.
- Establish evaluation frameworks, observability, and guardrails to make AI systems reliable and safe in production.
- Partner with product and business stakeholders to translate ambiguous problems into scoped, high-ROI AI solutions, and to judge when GenAI is (and isn't) the right tool.
- Set technical standards, review designs and code, and mentor mid-level data scientists and engineers.
- Stay current with a fast-moving field and bring well-filtered, practical recommendations to the team.

Required Qualifications

- 8 - 12 years in data science / ML, with a track record of production-deployed models and systems.
- Robust foundations in ML and statistics: supervised/unsupervised learning, experimentation and A/B testing,



model evaluation; familiarity with causal inference and time series.
- Expert Python (pandas, NumPy, scikit-learn), strong SQL, and proficiency in PyTorch or an equivalent DL framework.
- Demonstrated experience building GenAI applications: prompt engineering, RAG (chunking, embeddings, vector databases, reranking), and fine-tuning/adaptation (SFT, LoRA/PEFT) with sound judgment on when to use each.
- Hands-on experience designing agentic systems: tool/function calling, planning and orchestration, memory and state management, using frameworks such as LangChain/LangGraph, LlamaIndex, CrewAI, or AutoGen (and knowing when to build directly).
- Experience with LLM evaluation and observability (e.g., LLM-as-judge, golden datasets, tracing tools like LangSmith/Langfuse/Arize) and with guardrails for hallucination, safety, and prompt-injection defense.
- Production engineering skills: API design, serving and streaming, caching/batching, rate-limit handling, Docker, and CI/CD.

📌 Senior Artificial Intelligence Engineer / Lead Data Scientist (Pune)
🏢 Vitric Business Solutions
📍 Pune