Gen AI Engineer - 10th Sep (Thursday) - Video Interview

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.