• Design and develop AI agents and multi-agent workflows for enterprise use cases.
• Implement agent logic, orchestration flows, tool usage, and API-driven task execution.
• Build AI agents capable of handling task execution, workflow automation, and contextual interactions.
• Design, test, and optimize prompts, system instructions, and reusable prompt templates.
• Improve response quality, grounding, hallucination control, and output reliability.
• Implement fallback logic, retry mechanisms, and basic human-in-loop workflows.
• Implement Retrieval Augmented Generation (RAG) pipelines using enterprise data sources.
• Work with embeddings, vector databases, chunking strategies, and semantic search techniques.
• Support development of context-aware AI solutions integrating structured and unstructured data.
• Develop and integrate backend services and APIs for AI-based applications.
• Enable AI agents to interact with enterprise systems such as CRM, policy systems, workflow tools, and databases.
• Build scalable microservices to support AI solution deployment.
• Develop or integrate frontend interfaces such as chat-based UI, dashboards, or workflow interfaces for AI applications.
• Ensure seamless user experience for AI-powered applications.
• Optimize latency, response time, and performance of AI workflows.
• Monitor token usage, API performance, and system reliability.
• Support implementation of logging, performance monitoring, and evaluation mechanisms.
• Leverage AI-assisted development tools such as GitHub Copilot, Cursor, and enterprise copilots to enhance productivity.
• Contribute to reusable components, accelerators, and AI engineering best practices.
• Ensure adherence to enterprise standards for coding, security, and governance. Measures of Performance:
• Successful development and deployment of AI agents and workflows for assigned use cases.
• Quality and reliability of AI outputs (accuracy, grounding, reduced hallucinations).
• Adherence to performance benchmarks (latency, response time, and system stability).
• Efficiency in backend and frontend integration of AI solutions with enterprise systems.
• Effective implementation of RAG pipelines and data integration for contextual AI responses.
• Code quality, reusability of components, and contribution to shared AI engineering assets
• Adoption of AI-assisted development practices in day-to-day engineering work
• Timely delivery of use cases and responsiveness to business requirements
• Compliance with defined AI governance, security, and engineering standards
• Good understanding of LLMs, GenAI, and Agentic AI concepts, including multi-agent workflows.
• Hands-on experience with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Working knowledge of RAG pipelines, embeddings, vector databases, and semantic retrieval techniques.
• Strong experience in backend engineering using Python, FastAPI, REST APIs, and microservices architecture.
• Experience in frontend development (React, Angular, or similar) for building AI application interfaces.
• Familiarity with Azure OpenAI or similar LLM platforms and enterprise API integrations.
• Understanding of system integration, API orchestration, and enterprise application connectivity.
• Basic understanding of AI observability, monitoring, and token optimization.
• Awareness of security, governance, and responsible AI practices.
• Strong problem-solving ability and execution focus with attention to performance and scalability.
