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About the Role
Seeking a skilled AI Engineer to design, build, and deliver enterprise-grade AI, conversational AI, and Agentic AI solutions. This hands-on role involves enhancing user interaction with enterprise data, insights, recommendations, and business workflows. The position requires strong engineering capabilities in GenAI, AI/ML, data validation, backend development, and React application development, with end-to-end involvement from design to production support. You will architect and deliver multi-agent, tool-augmented GenAI solutions capable of reasoning, planning, contextual retrieval, and action execution across multiple enterprise data sources and platforms, ensuring secure, scalable, and governed AI systems.
Responsibilities
- Design, develop, and deploy production-grade GenAI solutions using advanced LLMs.
- Implement Retrieval-Augmented Generation (RAG) pipelines.
- Design hybrid search architectures combining Vector DBs and Graph DBs.
- Develop reusable AI components and frameworks.
- Build Agentic AI workflows including multi-agent orchestration, tool calling, function execution, and memory management.
- Perform end-to-end data validation for AI, ML, analytics, and decision-intelligence applications.
- Validate data completeness, accuracy, consistency, freshness, aggregations, calculations, and business-rule alignment.
- Work with business and data teams to validate KPIs, calculations, business rules, AI-generated insights, and recommendations.
- Develop automated data-quality checks, validation frameworks, anomaly detection, and reconciliation processes.
- Identify data-quality issues and coordinate with Data Engineering 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.
- Design and develop modern, responsive React-based web applications.
- 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.
- 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.
- 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 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.
- Remain directly accountable for assigned hands-on development activities.
- 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 services.
- Integrate applications with enterprise identity and access-management frameworks.
- Work closely with Cloud, Digital, Data Engineering, Architecture, Security, and Business teams.
- 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.
- Analyze AI application and agent performance.
- Optimize prompts, retrieval strategies, agent flows, APIs, database queries, and application performance.
- 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.
- Hands-on experience with relevant Azure and enterprise data technologies.
Skills
- GenAI
- AI/ML
- Data Validation
- Backend Development
- React Application Development
- LLMs
- Transformers
- Embedding
- Prompt Engineering
- Context Engineering
- RAG
- Agentic AI
- Multi-agent Orchestration
- Tool Calling
- Function Execution
- Memory Management
- Vector Databases
- Graph Databases
- Python
- SQL
- Spark
- JavaScript
- TypeScript
- FastAPI
- Flask
- PyTorch
- TensorFlow
- Azure
- 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
- DevOps