Senior AI Engineer - Data Scientist

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Role & responsibilities

• Design and implement classical ML solutions (regression, classification, ranking, time-series, anomaly detection) with feature engineering and appropriate validation.
• Lead development of GenAI products: LLM prompting, embeddings, vector search, RAG stacks, chain/agent design and safety/guardrails.
• Productionize models and LLM-based services (FastAPI/gRPC endpoints), containerize, deploy to cloud (Azure preferred; AWS/GCP ok).
• Collaborate with sales and engagement owners to scope pilots, estimate effort, and produce technical content for proposals and demos.
• Mentor engineers, run code reviews, improve onboarding docs and team playbooks.

Preferred candidate profile

• 5–8 years industry experience building ML solutions end-to-end with strong Python, PyTorch, PySpark expertise and software engineering best practices.
• Solid experience with classical ML toolset: scikit-learn, XGBoost / LightGBM / CatBoost, feature engineering, model evaluation & validation
• Hands-on experience with LLMs & GenAI: OpenAI / Azure OpenAI or Hugging Face; prompt engineering; embeddings.
• Experience building RAG pipelines and using vector DBs (Qdrant, Pinecone, PGVector).
• Familiarity with LangChain (or similar); ability to design multi-step agent workflows.
• Production deployment experience: Docker, Kubernetes and cloud infrastructure
• Good communication skills — capable of client demos + translating technical tradeoffs to non-technical stakeholders.