AI Engineer

• Design, develop, and deploy production-grade GenAI solutions using advanced LLMs such as OpenAI models.
  
• Implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured enterprise data.
  
• Design hybrid search architectures combining Vector DBs and Graph DBs such as Azure AI Search, Neo4j, and Databricks Vector Search.
  
• Develop reusable AI components and frameworks that can be leveraged across multiple enterprise AI use cases.
  
• Build Agentic AI workflows using frameworks such as LangChain, LangGraph, and Haystack, including:
  

1. Multi-agent orchestration including planner, retriever, evaluator, and executor agents.
   
2. Tool calling, function execution, and system-to-system automation.
   
3. Short-term, long-term, and session-based memory management.
   

• Perform end-to-end data validation before data is consumed by AI, ML, analytics, and decision-intelligence applications.
  
• Validate data completeness, accuracy, consistency, freshness, aggregations, calculations, and business-rule alignment across source systems and downstream applications.
  
• Work with business and data teams to validate KPIs, calculations, business rules, AI-generated insights, and recommendations before production release.
  
• Develop automated data-quality checks, validation frameworks, anomaly detection, and reconciliation processes.
  
• Identify data-quality issues and coordinate with Data Engineering and relevant teams for resolution.
  
• Ensure AI-generated insights and recommendations are based on validated and trusted enterprise data.
  

• Take hands-on ownership of end-to-end AI application development, from AI/ML services and APIs through user-facing applications.
  
• Design and develop modern, responsive react-based web applications for AI, analytics, and decision-intelligence use cases.
  
• Build interactive interfaces for AI insights, recommendations, conversational experiences, dashboards, visualizations, and actionable workflows.
  
• Integrate React applications with AI/ML services, enterprise APIs, data platforms, authentication services, and backend systems.
  
• Develop scalable backend services and APIs using Python and relevant API frameworks.
  
• Ensure frontend and backend applications meet enterprise requirements for performance, security, scalability, usability, and maintainability.
  

• Spend approximately 90% of the role as a hands-on Individual Contributor, directly involved in architecture, coding, development, debugging, testing, optimization, deployment, and production support.
  
• Allocate approximately 10% of the role to coordinating and reviewing technical tasks delivered by the offshore team.
  
• Review offshore team deliverables to ensure alignment with requirements, solution design, coding standards, and expected quality.
  
• Perform code reviews, technical reviews, and functional validation of assigned offshore deliverables.
  
• Provide clarification on technical requirements and tasks where required to support offshore delivery.
  
• Track assigned technical tasks and highlight dependencies, quality issues, or delivery risks.
  
• Work collaboratively with offshore AI Engineers, Data Engineers, Data Scientists, and Frontend Developers on integrated solution delivery.
  
• Remain directly accountable for assigned hands-on development activities while supporting the quality and integration of offshore deliverables.
  

• Develop and integrate AI-powered applications, chatbots, and agents within the Azure ecosystem.
  
• Integrate AI solutions with enterprise systems using APIs, event-driven architectures, and message brokers.
  
• Build secure and scalable services leveraging Azure App Services, Azure Functions, AKS, Azure Cache for Redis, and related services.
  
• Integrate applications with enterprise identity and access-management frameworks including authentication, authorization, RBAC, and data-level security.
  
• Work closely with Cloud, Digital, Data Engineering, Architecture, Security, and Business teams for end-to-end solution delivery.
  

• Implement guardrails for hallucination control, data privacy, security, responsible AI, and output validation.
  
• Ensure enterprise-grade governance including access control, auditability, monitoring, and compliance.
  
• Monitor production performance across availability, latency, accuracy, reliability, and scalability.
  
• Apply MLOps / LLMOps best practices across the lifecycle, including:
  

1. Model and version management.
   
2. Prompt versioning and rollback.
   
3. CI/CD pipelines for AI applications.
   
4. Automated prompt, retrieval, API, and regression testing.
   
5. Monitoring, logging, tracing, and observability.
   

• Analyze AI application and agent performance using metrics such as accuracy, response quality, latency, adoption, and task completion.
  
• Optimize prompts, retrieval strategies, agent flows, APIs, database queries, and application performance based on actual usage.
  
• Identify and resolve performance bottlenecks across data, AI, backend, database, and frontend layers.
  
• Drive continuous improvement through experimentation, evaluation, monitoring, and user feedback.
  

Requirements

• Strong hands-on understanding of LLMs, transformers, embedding, prompt engineering, context engineering, RAG, and evaluation techniques.
  
• Experience building end-to-end GenAI and Agentic AI products from development through production deployment.
  
• Hands-on experience with LangChain, LangGraph, Haystack, n8n, and Microsoft Copilot ecosystem or similar frameworks.
  
• Practical experience designing multi-agent architectures and orchestrating reasoning, retrieval, tools, and actions.
  
• Strong experience with Vector and Graph Databases, including Azure AI Search, Neo4j, and Databricks Vector Search.
  
• Proven experience implementing RAG pipelines using structured and unstructured enterprise data.
  
• Strong experience with data validation, data-quality checks, reconciliation, KPI validation, and business-rule validation.
  
• Strong proficiency in Python, SQL, and Spark & Hands-on experience developing React / JavaScript / TypeScript applications.
  
• Experience developing APIs and backend services using FastAPI, Flask, or equivalent frameworks
  
• Hands-on experience with PyTorch and/or TensorFlow.
  
• Experience working with large-scale AI/ML systems in production environments.
  
• Experience reviewing code and technical deliverables from distributed/offshore development teams.
  
• Strong problem-solving skills across AI, data, backend, frontend, and enterprise system integration.
  

• Azure OpenAI
  
• Azure Data Factory (ADF)
  
• Azure Databricks
  
• Azure AI Search
  
• Databricks Genie
  
• Azure AI Document Intelligence
  
• Azure App Services
  
• Azure Functions
  
• Azure Kubernetes Service (AKS)
  
• Azure Cache for Redis
  
• Azure Bot Service / Bot Framework
  
• API Management
  
• Microsoft Entra ID / Azure AD
  
• CI/CD and DevOps tooling