AI Engineer Lead

The Family Office Co.; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

The Family Office Co. seeks a Senior AI Engineer specialized in Microsoft Azure to lead the design, development, and deployment of agentic AI systems and GenAI platforms. You will guide architecture, MCP integrations, and MLOps foundations to deliver secure, compliant financial-grade AI solutions.

Role emphasizes leadership, architecture discipline, and hands-on delivery of Azure OpenAI, RAG, and vector-based pipelines within enterprise environments.

Qualifications

• Bachelor’s or Master’s in CS/AI/ML or related field.
• 10+ years in software/data/ML engineering with production AI/ML delivery.
• 3+ years delivering GenAI/LLM and RAG in production (Azure preferred).

Responsibilities

• Design and implement agentic AI solutions (autonomous workflows, multi-agent systems, tool-use) on Azure OpenAI and Semantic Kernel.
• Define and enforce architecture patterns for agents, tools, memory, and MCP integrations.
• Build modular GenAI pipelines for ingestion, transformation, and orchestration across LLMs and vector stores.

Skills

Azure OpenAI

LangChain

MCP (Model Context Protocol)

LLMOps

Python

TensorFlow/PyTorch

AKS

Azure Monitor

Education

Bachelor’s/Master’s in CS/AI/ML or related

Tools

Azure OpenAI Studio

Semantic Kernel

Cosmos DB

Redis/pgvector

Job description

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.