We are seeking a highly skilled and hands-on AI Engineer to design, build, and deliver enterprise-grade, production-ready AI, conversational AI, and Agentic AI solutions that enhance how users interact with enterprise data, insights, recommendations, and business workflows.
This is primarily a hands-on Individual Contributor (IC) role, with approximately 90% focus on individual technical contribution and 10% on coordinating and reviewing tasks delivered by the offshore development team.
The role requires strong engineering capabilities across GenAI, AI/ML, data validation, backend development, and React application development, with end-to-end involvement from solution design and coding through testing, deployment, and production support.
The role goes beyond traditional chatbots. 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. You will work on secure, scalable, and governed AI systems aligned with enterprise architecture and compliance standards, ensuring data quality, reliability, explainability, observability, and continuous improvement.
GenAI & Agentic AI Development :
- 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:
- Multi-agent orchestration including planner, retriever, evaluator, and executor agents.
- Tool calling, function execution, and system-to-system automation.
- Short-term, long-term, and session-based memory management.
Data Validation & Quality
- 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.
Full-Stack AI & React Application Development
- 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.
Individual Contribution & Offshore Team Coordination
- 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.
Enterprise Integration & Cloud Engineering
- 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.
Production Readiness, MLOps & LLMOps
- 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:
- Model and version management.
- Prompt versioning and rollback.
- CI/CD pipelines for AI applications.
- Automated prompt, retrieval, API, and regression testing.
- Monitoring, logging, tracing, and observability.
Performance Optimization & Continuous Improvement
- 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.
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