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

Job description

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.