Architect ML Engineer (GenAI)

We are looking for a hands-on Associate / Architect Generative AI to design, build, and deploy enterprise-grade GenAI platform capabilities across multiple business units. This role focuses on developing scalable and reusable AI components across the full stack, covering RAG systems, agent orchestration, LLM infrastructure, and GenAIOps on GCP (primary) and Azure. Key Responsibilities Design and build production-ready Generative AI systems and platform componentsDevelop and deploy scalable RAG pipelines including data ingestion, embeddings, retrieval, and APIsBuild agentic AI systems with orchestration, routing, memory, and workflow managementDevelop and manage LLM infrastructure including model routing, caching, observability, and rate limitingBuild scalable backend services and APIs for AI-driven applicationsImplement GenAIOps/MLOps practices including prompt management, evaluation, monitoring, and deploymentWork extensively with GCP services such as Vertex AI, BigQuery, Cloud Run, GKE, and Pub/SubEnsure AI governance, safety, compliance, PII protection, and auditability standards are maintainedDesign scalable enterprise AI architectures with strong focus on performance, reliability, and reusabilityCollaborate with cross-functional teams to deliver enterprise-grade AI solutionsMentor junior engineers and contribute to technical leadership, architecture discussions, and design reviewsRequired Skills & Experience Strong hands-on experience building and deploying production-grade Generative AI and RAG systemsExperience working on multi-agent or agentic AI architecturesStrong proficiency in Python and backend/API developmentHands-on experience with GCP AI/ML ecosystem including Vertex AI and BigQuerySolid understanding of LLM infrastructure, orchestration layers, and AI platform engineeringExperience with CI/CD pipelines and Infrastructure as Code tools like TerraformGood understanding of GenAIOps/MLOps practices and model lifecycle managementStrong system design and architecture experience for scalable AI platformsExposure to enterprise application architecture and distributed systemsExperience leading small engineering teams, mentoring developers, or owning technical delivery is preferredUnderstanding of AI safety, governance, and compliance best practicesNice to Have Experience with LangChain, LlamaIndex, or similar frameworksFamiliarity with RAG evaluation tools such as RAGAS or DeepEvalKnowledge of Knowledge Graphs with RAG systemsExperience working in multi-cloud environments (GCP + Azure)Exposure to BFSI or other regulated domainsWhat Were Looking For Engineers who have built and deployed real-world GenAI systems at scaleStrong backend engineering and systems-thinking mindsetAbility to thrive in fast-paced enterprise environmentsOwnership mindset with strong communication and collaboration skills .