Description

Key ResponsibilitiesBuild end-to-end GenAI applications using LLMs (text| documents| QA| summarization| extraction)Implement RAG pipelines data ingestion| chunking| embeddings| vector search| and grounded responsesDevelop APIs and microservices to integrate GenAI solutions with enterprise systemsImplement structured outputs| function calling| and prompt frameworksSupport evaluation| testing| monitoring| and continuous improvement of GenAI solutionsCollaborate closely with the GenAI Architect and Agentic AI EngineersCore SkillsHands-on experience with LLMs and RAG architecturesExperience with vector databases and semantic searchPractical understanding of prompt engineering and evaluation 2) Agentic AI Engineers (Autonomous AI Systems)Role Purpose Build Agentic AI systems that go beyond chatbots  autonomous| goal-driven AI agents that can reason| plan| call tools| and execute multi-step workflows.Key ResponsibilitiesDesign and implement agentic AI workflows (planning  execution  observation  iteration)Build tool-using AI agents integrated with APIs| data sources| and enterprise systemsImplement multi-agent or orchestrated agent architectures where requiredCombine Agentic AI with RAG for grounded| enterprise-safe responsesImplement guardrails| safety checks| and evaluation mechanismsDebug| optimize| and operationalize agentic workflows in productionCore SkillsHands-on experience building agentic or multi-step AI workflows (not just prompting)Experience with orchestration frameworks and tool callingUnderstanding of AI safety| guardrails| and reliability patternsAbility to operationalize AI agents at enterprise scale3) GenAI Architect  AI Coach (Architecture  Enablement)Role Purpose Act as the technical anchor and AI coach for the program  defining the GenAI architecture| standards| and guardrails| while guiding developers and ensuring long-term scalability

Key Skills

Education

Bachelor's degree