Must have

- Generative AI production-grade GenAI solution design and deployment

- Agentic AI / Multi-Agent Systems agent orchestration, tool-using agents, memory-enabled systems

- Advanced RAG Architecture multi-stage retrieval, re-ranking, multi-hop retrieval and reasoning

- Python (core), FastAPI, React

- Vector Databases & Embedding Models hybrid search architectures

- LLM Integration – LLM APIs, multi-model AI architectures

- AI Platform Engineering – model serving, feature stores, GPU/infra readiness

- Production AI Deployment – observability, logging, tracing, reliability engineering, graceful degradation, circuit breakers

- AI Evaluation Frameworks – A/B testing, benchmarking, telemetry-based optimization

- Prompt Engineering – templates, versioning, testing methodologies

- Observability & Monitoring – real-time dashboards, automated alerting, incident response

- Enterprise/Cloud-Native Architecture – distributed systems at scale

- Cloud AI Platforms – GCP/Azure

- With loops & graphs hands on

Roles & Responsibilities

- Architect end-to-end AI systems including advanced RAG pipelines, multi-agent orchestration frameworks, and multi-model AI integrations built for modularity, scalability, and operational excellence.





- Define enterprise standards for prompt engineering (templates, versioning, testing, evaluation) and performance optimization (model selection, caching, resource utilization, cost).

- Lead deployment of AI solutions into production with comprehensive observability, reliability engineering, monitoring dashboards, automated alerting, and incident response — meeting stringent SLOs.

- Design scalable data ingestion frameworks for structured, unstructured, and real-time streaming data, along with vector database architectures, hybrid search, preprocessing pipelines, and data quality/governance frameworks.

- Establish quantitative AI evaluation frameworks (A/B testing, benchmarking, user feedback, telemetry) and drive continuous improvement across prompts, retrieval strategies, agent workflows, and model configurations.

- Partner with platform and infrastructure teams on AI workload readiness (GPU infra, model serving, feature stores, storage, networking) and define enterprise AI platform requirements.

- Ensure AI solutions adhere to enterprise governance and compliance; apply Responsible AI principles — fairness, transparency, accountability, and bias mitigation.

📌 Gen AI Engineer - 10th Sep (Thursday) - Video Interview (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru