Python +AI Engineer
Job Description:
We are hiring a Full Stack Developer with strong expertise in LLMs and Agentic AI systems to build and scale an enterprise-grade AI-powered SDLC automation platform. This is not a traditional full stack role — we are looking for engineers who can design, orchestrate, and optimize AI agents using AWS Bedrock, advanced prompt engineering, and LLM context/memory management, while also contributing across backend, frontend, and cloud infrastructure.
Exp: 10+yrs
Roles & Responsibilities:
1. Agentic AI System Development:
- Design and implement multi-agent architectures powering SDLC automation
- Build intelligent agents for- Requirement grooming, Code generation, Testing automation & Impact analysis
- Optimize agent decision-making, orchestration, and outputs
2. Cloud & AI Infrastructure:
- Develop AI-driven applications using AWS Bedrock
- Integrate foundation models and manage inference pipelines
- Build scalable AI services for enterprise use
3. Full Stack Development:
- Develop scalable applications using- Frontend (React) & Backend (Python)
- Build APIs to orchestrate AI agents and workflows
- Design microservices for distributed agent execution
4. Containerization & Deployment:
- Build and maintain Dockerized applications
- Deploy and manage services on Kubernetes
- Ensure scalable and resilient infrastructure
5. DevOps & CI/CD:
- Build and maintain CI/CD pipelines
- Automate testing, integration, and deployments
- Enable continuous delivery of AI-enabled features
6. Integration & Ecosystem (MCP Focus)
- Build integrations using Model Context Protocol (MCP) with GitHub / Git, JIRA and Other enterprise tools
- Enable Automated code workflows, Issue creation and lifecycle tracking, Context-aware agent interactions across systems
7. Performance & System Engineering:
- Optimize AI system performance, Memory usage & Latency in agent execution
- Design scalable multi-tenant systems with RBAC and governance
Required Skills:
- AI & LLM Engineering.
- Strong hands-on experience with AWS Bedrock.
- Advanced Prompt Engineering: Prompt structuring, chaining, and optimization, multi-agent prompt design and Task decomposition for agent workflows.
- Deep understanding of LLM Context Management (token limits, context windows, retrieval strategies) & Memory Management in AI systems (short-term, long-term, vector DBs).
- Experience designing and building with Autonomous AI Agents / Agentic Workflows and Multi-agent collaboration systems.