Key Responsibilities

- Lead design and development of Python-led Agentic AI solutions, including agent workflows, orchestration, tool calling, function routing, memory/state management and multi-agent coordination.
- Define and implement LLM-enabled solution patterns using prompt engineering, structured outputs, RAG, vector databases, semantic search and enterprise grounding approaches.
- Evaluate and recommend agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel and CrewAI based on use case complexity, maintainability, scalability and enterprise readiness.
- Own integration design across APIs, microservices and enterprise platforms such as ServiceNow, Appian, SAP, Salesforce, Microsoft 365, Teams and Power Platform.
- Drive cloud and DevOps readiness using Azure, AWS or GCP, including Docker, Kubernetes, CI/CD, GitOps, infrastructure automation and deployment governance.
- Establish testing, observability and LLM evaluation practices covering AI output quality, tracing, monitoring, performance, cost optimization and production support readiness.
- Embed responsible AI, security, identity, compliance and guardrail requirements into solution design, including OAuth, Azure AD, data protection and enterprise risk controls.
- Coach junior engineers, review designs and code, unblock technical delivery, and enable RDE POD members to operate as T-shaped, multi-skilled contributors.

Must Have Skills

- Python & Full-Stack Development
- Agentic AI (LangChain, LangGraph, MCP, RAG)
- Robust Python engineering experience including async programming, Object-Oriented Programming, data structures, scripting and production-grade AI pipeline development.




- Hands-on experience with GenAI / LLM solution development, prompt engineering, structured outputs, RAG pipelines, vector databases and semantic search.
- Practical understanding of Agentic AI design patterns including orchestration, tool use, function calling, state management, memory and multi-agent coordination.
- Experience with at least one relevant AI framework such as LangGraph, LangChain, Semantic Kernel or CrewAI.
- API and microservices development experience using REST, GraphQL or equivalent enterprise integration patterns.
- Cloud-native deployment exposure across Azure, AWS or GCP, with working knowledge of Docker, Kubernetes, CI/CD and Git-based engineering practices.
- Ability to design security, responsible AI, observability, scalability, resiliency and production support.
- Proven ability to lead small engineering teams or PODs, conduct technical reviews and guide junior resources.

Secondary Skills

- Enterprise integration exposure with ServiceNow/Appian/SAP/Salesforce/Microsoft 365/Teams or Power Platform.
- Experience with LLMOps, AI testing, model behavior evaluation, prompt regression testing, monitoring dashboards and cost governance.
- Knowledge of responsible AI controls, AI guardrails, bias mitigation, audit logging, compliance expectations and data privacy needs.
- Working understanding of DevSecOps, GitOps, API gateways, cloud security and identity management using OAuth or Azure AD.
- Ability to translate business problems into solution architecture, reusable implementation patterns and delivery-ready technical backlogs.
- Exposure to agile delivery, stakeholder collaboration, documentation and production support operating models.

📌 RDE- Analytics Developer | Team Lead (Bengaluru)
🏢 TRIGENT SOFTWARE PRIVATE
📍 Bengaluru