Role Overview
We are looking for an experienced Generative AI / Azure AI Engineering Lead with 11+ years of experience in IT, AI Engineering, Machine Learning, Generative AI, and cloud-based AI solution delivery. The candidate will be responsible for designing, developing, and deploying enterprise-grade AI solutions using Azure OpenAI, Microsoft Foundry / Azure AI Foundry, Azure AI Search, RAG, Agentic AI, Python, LangChain, Azure Functions, and cloud-native Azure services.
The role requires strong hands-on expertise in building production-ready Generative AI platforms involving retrieval-augmented generation, enterprise knowledge search, document intelligence, multi-agent workflows, structured AI outputs, evidence-based citations, workflow automation, and API-driven integrations.
The candidate should be capable of converting complex business requirements into scalable AI architectures, leading technical teams, defining solution blueprints, and delivering client-facing AI solutions for enterprise stakeholders.
Key Responsibilities
Lead the architecture, design, development, and implementation of enterprise Generative AI solutions on Microsoft Azure.
Design and develop AI applications using Azure OpenAI, Microsoft Foundry / Azure AI Foundry, Azure AI Search, Azure Functions, Azure Blob Storage, Azure Content Understanding, and Azure Machine Learning.
Build end-to-end RAG-based applications using document ingestion, chunking, embeddings, vector search, hybrid search, semantic ranking, metadata filtering, and citation-grounded response generation.
Design and implement Agentic AI and multi-agent architectures using orchestrator agents, specialist agents, tool-based workflows, system prompts, structured prompts, and deterministic business logic.
Integrate Azure OpenAI models with enterprise applications for document summarization, knowledge retrieval, intelligent Q&A, business decision support, content generation, and workflow automation.
Develop Python-based orchestration pipelines for agent execution, business rule processing, scoring logic, evidence mapping, structured JSON response generation, exception handling, and API integration.
Implement document intelligence workflows using OCR, text extraction, metadata enrichment, prebuilt/custom analyzers, content chunking, embedding generation, and vector indexing pipelines.
Configure and optimize Azure AI Search indexes for vector retrieval, keyword search, hybrid search, semantic search, filtering, ranking, and source traceability.
Design secure and scalable cloud-native AI solutions using Managed Identity, Azure AD / Entra ID, Azure Key Vault, RBAC, API security, logging, and monitoring.
Collaborate with business stakeholders, product owners, architects, data engineers, application teams, and client teams to translate business requirements into scalable AI solution designs.
Define solution architecture, integration patterns, data flow, security model, deployment approach, and implementation roadmap for Generative AI solutions.
Ensure AI responses are reliable, traceable, and business-ready through prompt engineering, grounding, hallucination control, citation validation, structured output design, and evaluation frameworks.
Lead technical teams across design, development, testing, debugging, deployment, production readiness, and support activities.
Troubleshoot and optimize AI pipelines related to retrieval quality, prompt accuracy, model performance, token usage, vector indexing, search relevance, latency, and response reliability.
Prepare technical documentation, architecture diagrams, implementation plans, proof-of-concepts, client-facing walkthroughs, and solution presentations.
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Required Skills
Generative AI & LLM Engineering
Strong experience with Azure OpenAI, GPT models, LLM orchestration, prompt engineering, system prompts, function calling, structured output generation, and LLM-based application development. Hands-on experience in building RAG pipelines, Agentic AI workflows, multi-agent architectures, and enterprise chatbot solutions. Strong understanding of hallucination control, citation grounding, AI safety guardrails, model evaluation, response validation, and prompt optimization.
Azure AI & Cloud Services
Strong hands-on experience with Microsoft Foundry / Azure AI Foundry, Foundry Agents, Azure OpenAI Service, Azure AI Search, Azure Content Understanding, Azure Machine Learning, Azure Functions, Azure Blob Storage, Azure Key Vault, Azure Monitor, and Application Insights. Experience in designing cloud-native AI solutions with Managed Identity, Azure AD / Entra ID, RBAC, secure API access, logging, monitoring, and enterprise-grade access control.
RAG & Enterprise Knowledge Search
Experience in designing and implementing document ingestion pipelines, chunking strategies, embedding generation, vector indexing, vector search, hybrid search, semantic ranking, metadata filtering, and ACL-based retrieval. Strong understanding of SharePoint integration, enterprise content grounding, source traceability, document versioning, citation generation, and evidence-based AI responses.
Programming & Frameworks
Strong programming experience in Python. Hands-on experience with FastAPI, LangChain, LlamaIndex, Promptflow, REST APIs, JSON schema design, SQL, PostgreSQL, Supabase, Git, CI/CD, Docker, and Azure Container Apps. Ability to design backend services, orchestration layers, reusable AI components, and API-driven integrations.
Machine Learning & NLP
Strong understanding of Machine Learning, NLP, text classification, named entity recognition, semantic similarity, document classification, OCR pipelines, embeddings, and model deployment. Experience with ML/NLP techniques and models such as BERT, Word2Vec, TF-IDF, LSTM, RNN, XGBoost, supervised learning, and MLOps practices.
Required Experience
11+ years of overall IT experience in application development, AI engineering, machine learning, cloud solutions, or data-driven application delivery.
Hands-on experience in designing and deploying Generative AI solutions on Microsoft Azure.
Practical experience with Azure OpenAI, Azure AI Search, RAG, prompt engineering, embeddings, vector databases, and enterprise knowledge search.
Experience in building multi-agent AI solutions or agentic workflows using orchestrator and specialist-agent patterns.
Experience in integrating AI solutions with enterprise applications, databases, APIs, SharePoint, cloud storage, and business workflows.
Experience leading technical teams and working with business stakeholders, architects, product owners, and client teams.
Strong experience in preparing architecture diagrams, technical design documents, implementation plans, and client-facing solution materials.
Preferred Experience
Experience in enterprise domains such as government, ports and logistics, KYC, compliance, legal, finance, procurement, enterprise knowledge management, or customer service automation.
Experience with document intelligence, video RAG, SharePoint-based knowledge retrieval, Microsoft Teams agents, and enterprise chatbot platforms.
Experience in designing AI systems that require structured JSON outputs, scoring logic, decision support, risk assessment, evidence mapping, and source-level traceability.
Experience with Snowflake, PostgreSQL, Supabase, Cosmos DB, Azure Blob Storage, and external API integrations.
Experience with production-readiness activities including testing, monitoring, telemetry, logging, performance tuning, and AI response evaluation.
Tools & Technologies
Cloud & AI
Microsoft Foundry / Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, Azure Content Understanding, Azure Functions, Azure Blob Storage, Azure Key Vault, Azure Monitor, Application Insights
Frameworks
LangChain, LlamaIndex, Promptflow, FastAPI
Programming
Python, SQL, REST APIs, JSON, Git
Data & Storage
PostgreSQL, Supabase, Snowflake, Cosmos DB, Azure Blob Storage
AI/ML
RAG, LLMs, embeddings, vector search, NLP, BERT, LSTM, XGBoost, OCR, NER
Security
Azure AD / Entra ID, Managed Identity, RBAC, ACL-based retrieval, API security
DevOps
Git, CI/CD, Docker, Azure Container Apps, logging, monitoring, and deployment automation
Expected Outcomes
Deliver scalable, secure, and production-ready Generative AI solutions on Microsoft Azure.
Build reliable RAG and Agentic AI applications with strong grounding, citation traceability, and hallucination control.
Design enterprise-grade AI architectures that integrate with business systems, document repositories, databases, APIs, and cloud services.
Lead technical delivery from solution design to implementation, testing, deployment, and production support.
Support client-facing discussions, demos, technical walkthroughs, solution documentation, and delivery planning.
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