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.