Engineering Manager_AI Architect_9+ years_Pune

TH210726_105189_JD_AI/ML Job Description: Role Summary The senior AI engineer is responsible for turning validated proof of concepts into reliable, scalable and maintainable production software, and give technical direction on best practices on AI, to other colleagues. You will work on a mix of: LLM-based solutions (API-based models, RAG, agents). Classical ML / DL systems (forecasting, anomaly detection, computer vision). Senior AI Engineer is expected to work independently with minimal guidance, own solutions end to end and contribute to giving technical direction on best practices and latest technologies across the teams portfolio. Key Responsibilities Translate business problems into robust AI use cases with measurable performance KPIs. Develop enterprise grade AI solutions. Conceptualize and design enterprise grade AI solutions. Drive the choice of technical solution with arguments around best-practices, reusability, risks, advantages, quick-wins etc. Implement end-to-end AI solutions from problem framing to production. Implement test harnesses and monitoring on the built solutions. Build and operate LLM-powered systems (system instructions, context, RAG, evaluation, guardrails etc.) Develop, train, and deploy ML/DL models where appropriate. Ensure AI solutions are reliable, scalable, secure, and maintainable. Skills and qualifications: Software-Engineering Foundation - Mandatory Minimum 5 years of experience in software development. Strong programming skills in Python. Disciplined engineering habits: version control (Git), code review, automated testing, and clear documentation. Designing and consuming REST APIs; building services with FastAPI or an equivalent framework. Comfort working with structured data and databases, including writing solid SQL. Artificial Intelligence skills - Mandatory Demonstrable experience building applications on LLMs - shipped LLM-backed features, in a product. Sound judgement and understanding for model selection, cost optimization, latency and trade-offs. Strong understanding of orchestration frameworks, e.g. LangChain. At least a strong understanding of RAG, Guardrails, chunking, and vector search and grounding. AIOps/MLOps - Mandatory Experience with operationalizing AI and ML workloads taking from prototypes to monitored, maintainable production service. Working with a managed LLM platform in production - Azure OpenAI / Azure AI Foundry strongly preferred (e.g. GPT-4o and managed embedding models). Familiarity with secure handling of credentials and configuration (e.g. managed identity, key-vault etc.). Nice to have: Experience with machine learning algorithms for regression and classification. Front-end familiarity (React / TypeScript) for prototyping AI-facing interfaces. Containerization (Docker) and CI/CD experience. Familiarity with responsible AI, explainability, and governance concepts. Data analysis and visualization skills. Job Description: .