AI Engineer Lead

AI Engineer Lead Role Summary We are seeking a Senior AI Engineer specialized in Microsoft Azure to lead the design, development, and deployment of agentic AI systems and enterprise-grade GenAI platforms . You will serve as a technical leader—defining architecture patterns (including Model Context Protocol – MCP ), building RAG and LLMOps foundations, and ensuring solutions meet financial-services-grade security, compliance, and reliability standards. Key Responsibilities Agentic AI Architecture & Development Design and implement agentic AI solutions (autonomous workflows, multi-agent systems, tool-use) using Azure OpenAI / Azure AI Studio and Semantic Kernel/Lang Chain/Lang Graph . Define and enforce architecture patterns for agents, tools, memory, and MCP integrations. Implement schema-constrained prompting , output validation, and guardrails to reduce hallucinations and enforce safety. GenAI Pipelines & RAG Build modular pipelines for ingestion, transformation, and orchestration across LLMs, embedding services, vector stores, SQL sources, and APIs. Develop hybrid RAG using Azure AI Search (vector & hybrid) plus structured sources (e.g., Azure SQL/SQL MI , Synapse , APIs). Implement post-retrieval re-ranking , chunking strategies, feedback/DPO-based ranking, and caching (KV) for latency & cost control. Monitor performance and quality with Langfuse/Prompt Flow traces , Azure Monitor , and Prometheus/Grafana . AI Platform & Services Architect platform components: embedding pipelines , vector stores (Azure AI Search vectors, Cosmos DB with vector support , Redis/pgvector on Azure), document intelligence (Azure Document Intelligence / OCR), and metadata extraction . Define API standards and integration patterns (REST/GraphQL/gRPC) backed by Azure API Management . Cloud Architecture & Infrastructure (Azure-first) Lead cloud architecture on Azure for scalability, security, and cost: AKS , Azure Functions , Event Hubs (Kafka-compatible) , Service Bus , Data Lake Storage Gen2 , Blob Storage , Key Vault , Private Link/VNet . Productionize models using Azure Machine Learning (model registry, managed endpoints, Prompt Flow, MLflow integration). Establish infrastructure standards for containerization (Docker) , orchestration (AKS / ARO-OpenShift) , monitoring , and MLOps/LLMOps . Engineering Excellence & Leadership Lead design reviews, code reviews, and CI/CD (GitHub Actions or Azure DevOps) with automated prompt tests, guardrail tests, and regression suites. Mentor AI engineers; build internal best practices and knowledge assets. Define SLOs/SLAs and implement proactive alerting, rate-limits, and fallbacks. Stakeholder & Governance Translate business requirements into technical designs with AI Product Owners and domain stakeholders. Partner with Security, Risk, and Enterprise Architecture on standards, privacy, and Responsible AI practices (e.g., Azure Content Safety). Required Skills & Qualifications Education Bachelor’s/master’s in computer science, AI/ML , Data/Software Engineering , or related field. Experience 10+ years in software/data/ML engineering, including hands-on production delivery of AI/ML solutions. 3+ years delivering GenAI/LLM and RAG systems in production (Azure preferred). Proven leadership of complex technical initiatives in enterprise or financial services environments. Technical Skills (Azure-Focused) LLMs & GenAI: Azure OpenAI (GPT, vision, embeddings), model selection, prompt engineering, function/tool calling, LLMOps (prompt versioning, evaluation, output validation). Orchestration: Semantic Kernel , Lang Chain/LangGraph , tool routing, multi-hop workflows, MCP . RAG & Vectors: Azure AI Search (hybrid+vector), Cosmos DB vectors/Redis/pgvector, FAISS/Qdrant (self-hosted on AKS). Data & Ingestion: Azure Document Intelligence (Form Recognizer/OCR), Cognitive Services, Azure Data Factory/Synapse pipelines, APIs/SQL; layout-aware parsing & chunking. MLOps: Azure ML (registries, pipelines, endpoints), MLflow , feature/model/version management, A/B and shadow deployments. Cloud & Infra: AKS/ARO , Docker, Event Hubs (Kafka) , Service Bus, Functions, API Management, Key Vault, Monitor, Log Analytics, Application Insights, Managed Identities, VNet/Private Link. Programming: Python (advanced) ; experience with Transformers , Sentence Transformers , PyTorch . Services & APIs: REST/gRPC design, auth (Entra ID/OAuth2), RBAC, multi-tenant patterns; caching and rate limiting. Observability: Azure Monitor , Prometheus/Grafana , Langfuse or equivalent. Industry & Governance Experience in financial services with understanding of regulatory , model risk management , security , privacy , and auditability requirements. Department AI & Data Science Role AI Engineer Locations Bangalore Remote status Hybrid About The Family Office Company The Family Office is an independent wealth management firm offering customized investment solutions in alternative asset classes, including private equity, private debt, and real estate. Serving high-net-worth individuals and families, we provide tailored strategies to address unique financial needs with a focus on transparency, diversification, and long-term value. With a commitment to excellence and decades of expertise, The Family Office helps clients preserve and grow their wealth across generations. Founded in 2004 Co-workers 120+