AI Engineer
Role summary
We are seeking an experienced AI Engineer to build and deploy cutting-edge LLM-powered applications. This role involves developing generative AI solutions using Azure AI Foundry and Azure OpenAI, implementing orchestration logic with LangChain and LangGraph, and engineering RAG and NLP pipelines. You will leverage your strong Python development skills to create production-grade services, optimize performance, and ensure AI safety and reliability. Experience with Azure services, vector databases, REST APIs, and CI/CD pipelines is essential. The ideal candidate will translate solution designs into working code, collaborate effectively with cross-functional teams, and possess strong problem-solving and communication skills.
Primary Skills: Python, LLM Models, Restful services,CI-CD, Azure stack
Key Responsibilities
- Generative AI & LLM Application Development
• Build and deploy LLM powered applications using:
o Azure AI Foundry (projects, prompt flows, evaluations, deployments)
o Azure OpenAI (chat, embeddings, assistants/agents)
• Implement use cases such as:
o Conversational AI and copilots
o Knowledge assistants and Q&A systems
o Intelligent document understanding and summarization
• Integrate AI capabilities into enterprise applications via APIs and microservices [learn.microsoft.com], [ca.linkedin.com]
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- LangChain & LangGraph Implementation
• Develop LLM orchestration logic using:
o LangChain chains, tools, retrievers, and memory
o LangGraph for graph based, stateful workflows
• Implement:
o Multi step reasoning workflows
o Tool calling and function execution
o Supervisor/worker and multi agent patterns
• Manage conversational state, context windows, and memory persistence [learn.microsoft.com], [dice.com]
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- RAG & NLP Engineering
• Build Retrieval Augmented Generation (RAG) pipelines using:
o Azure AI Search (vector and hybrid search)
o Embeddings from Azure OpenAI / Foundry model catalog
• Engineer NLP pipelines for:
o Text extraction, classification, summarization
o Entity recognition and semantic search
• Curate, chunk, embed, and validate enterprise knowledge sources for accuracy and freshness [learn.microsoft.com], [dice.com]
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- Python Based AI Engineering
• Develop production grade Python services and libraries for AI workloads
• Implement:
o API layers and orchestration services
o Async execution, streaming, and batching
• Optimize:
o Latency, cost, and token usage
o Prompt structure and context management
• Follow engineering best practices for testing, versioning, and modularity [dice.com]
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- Safety, Reliability & Governance
• Implement guardrails and safety controls:
o Content filtering and moderation
o Prompt validation and response checks
• Build evaluation and feedback loops:
o Accuracy, relevance, and hallucination detection
o Regression testing for prompts and workflows
• Ensure compliance with enterprise security and data handling standards [learn.microsoft.com], [dice.com]
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- Cloud & DevOps Integration
• Deploy AI workloads using:
o Azure App Service, Azure Functions, or AKS
• Integrate with:
o Azure Entra ID (authentication and authorization)
o CI/CD pipelines and observability tools
• Support monitoring, logging, and traceability of AI workflows in production [learn.microsoft.com]
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Required Skills & Experience
Core Skills
• 5–8+ years of software engineering experience
• Strong hands on Python development experience
• Practical experience building applications using:
o Azure AI Foundry
o Azure OpenAI Service
o LangChain and LangGraph
• Solid understanding of LLMs, RAG, and NLP concepts [dice.com]
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Azure & AI Stack
• Azure services:
o Azure AI Foundry
o Azure AI Search
o Azure OpenAI
• Experience with:
o Vector databases and embeddings
o REST APIs and microservices
• Familiarity with DevOps and cloud native deployment models
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Soft Skills
• Ability to translate solution designs into working code
• Strong problem solving and debugging skills
• Effective collaboration with architects and cross functional teams
• Clear documentation and communication skills
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Nice to Have
• BFSI or regulated industry experience
• Exposure to:
o MLOps / LLMOps
o Evaluation frameworks and observability tooling
• Experience with Responsible AI and governance frameworks
• Azure AI or cloud certifications
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