AI ML Engineer with python

About the role

We are seeking an experienced AI Engineer to design, develop, deploy, and support enterprise-grade Artificial Intelligence, Machine Learning, Generative AI, and Large Language Model (LLM) solutions. The ideal candidate will have strong hands-on experience with Python, Generative AI, RAG, LLMs, Azure OpenAI, Azure AI Search, vector databases, AI agents, document intelligence, REST APIs, and cloud-native application development. The candidate will work closely with engineering, architecture, data, security, and business teams to build scalable AI solutions that integrate enterprise data and applications.

Responsibilities

• Design, develop, test, deploy, and maintain production-grade AI/ML and Generative AI applications.
• Build LLM-powered applications using Azure OpenAI, OpenAI APIs, or other enterprise AI platforms.
• Design and implement Retrieval-Augmented Generation (RAG) solutions for enterprise knowledge and document search.
• Develop AI-powered document processing, classification, extraction, summarization, and validation pipelines.
• Build intelligent AI agents using LangChain, LangGraph, Semantic Kernel, or similar frameworks.
• Implement agentic workflows including tool calling, orchestration, state management, context engineering, and structured outputs.
• Develop Python-based AI services, APIs, automation frameworks, and reusable components.
• Build and integrate REST APIs, microservices, and enterprise applications.
• Implement semantic, keyword, vector, and hybrid search capabilities.
• Develop embedding pipelines and integrate vector databases such as Pinecone, FAISS, Weaviate, ChromaDB, PostgreSQL/pgvector, or Azure AI Search.
• Perform prompt engineering, prompt evaluation, context optimization, and response-quality improvement.
• Implement document intelligence solutions using OCR, document extraction, classification, and entity recognition.
• Work with structured and unstructured enterprise data.
• Integrate AI solutions with SQL databases, APIs, data platforms, and enterprise applications.
• Develop model evaluation frameworks to measure accuracy, relevance, latency, cost, and reliability.
• Implement AI guardrails, security controls, monitoring, logging, and observability.
• Containerize AI applications using Docker and deploy them using Kubernetes/AKS or similar platforms.
• Participate in CI/CD implementation using Azure DevOps, GitHub Actions, Jenkins, or equivalent technologies.
• Troubleshoot production AI applications and improve performance and scalability.
• Collaborate with architects, developers, data engineers, cybersecurity teams, and business stakeholders.
• Participate in Agile/Scrum ceremonies, sprint planning, code reviews, and technical discussions.
• Create technical documentation covering architecture, implementation, APIs, AI workflows, and operational procedures.

Required Technical Skills

Programming

• Strong hands-on experience with Python
• Python APIs and microservices
• FastAPI or Flask
• REST APIs
• JSON
• Pydantic
• Unit testing and debugging
• Git/GitHub

Generative AI / LLM

• Large Language Models (LLMs)
• Generative AI
• Prompt Engineering
• Context Engineering
• Structured Outputs
• Function Calling / Tool Calling
• AI Agents
• Agentic AI
• Multi-step AI workflows
• LLM evaluation
• AI guardrails

RAG & Enterprise Search

• Retrieval-Augmented Generation (RAG)
• Embeddings
• Semantic Search
• Vector Search
• Hybrid Search
• Keyword Search
• Query Rewriting
• Reranking
• Chunking strategies
• Metadata filtering
• Knowledge retrieval

AI / Cloud Platforms

• Hands‑on experience with one or more: Azure OpenAI, Azure AI Search, Azure AI Foundry, Azure AI Document Intelligence, Azure Cognitive Services, AWS Bedrock, OpenAI APIs, Google Vertex AI

AI Frameworks

• Experience with one or more: LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI

Vector Databases

• Experience with one or more: Pinecone, FAISS, Weaviate, ChromaDB, Milvus, PostgreSQL / pgvector, Azure AI Search

Data & Databases

• SQL, PostgreSQL, SQL Server, Oracle, MongoDB
• Structured and unstructured data
• Data pipelines
• ETL/ELT concepts

Cloud & DevOps

• Microsoft Azure
• Docker
• Kubernetes / AKS
• Azure DevOps
• GitHub Actions
• CI/CD
• Application monitoring
• Logging and observability

Document Intelligence Experience

• Experience developing solutions for: PDF and document ingestion, OCR processing, document classification, text extraction, entity extraction, metadata extraction, document summarization, document comparison, structured data extraction, validation workflows, intelligent document search
• Experience with Azure Document Intelligence, PyMuPDF, Tesseract OCR, or similar technologies is a plus.

AI Agent Experience

• Understanding of modern agentic AI architectures, including: AI agents, tool calling, function calling, agent orchestration, planner/executor patterns, multi‑agent workflows, state management, memory/context management, structured responses, human‑in‑the‑loop workflows, agent evaluation, guardrails and safety controls

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or related field.
• 5+ years of professional software/technology experience.
• 3+ years of hands‑on AI/ML, Generative AI, Machine Learning, or AI application development experience.
• Strong Python development experience.
• Production experience building AI/LLM applications.
• Experience with RAG and vector/semantic search.
• Experience integrating AI solutions with APIs and enterprise systems.
• Strong understanding of software development lifecycle and Agile methodologies.
• Strong communication and problem‑solving skills.
• Ability to work in a collaborative enterprise environment.

Preferred Qualifications

• Experience with financial services, banking, insurance, healthcare, or other regulated industries.
• Experience with enterprise document intelligence.
• Experience with knowledge graphs or graph databases.
• Experience with Azure Kubernetes Service (AKS).
• Experience with AI observability and LLMOps.
• Experience with model evaluation and quality metrics.
• Experience with NLP, NER, text classification, and information extraction.
• Experience with React/TypeScript or other frontend technologies is a plus.
• Experience with enterprise security, privacy, governance, and compliance.

Soft Skills

• Excellent verbal and written communication.
• Strong analytical and problem‑sol