AI Software Engineer (Agentic AI & MCP Systems)
Key Responsibilities
Design, develop, and maintain MCP servers using Python and/or TypeScript to support scalable and standardized LLM interactions.
Build and deploy agentic AI systems capable of planning, reasoning, and executing multi-step workflows.
Implement agent workflows using:
Tool / function calling
Short-term and long-term memory
Context management
Guardrails and controlled execution
Develop system with agents orchestrates workflows with LLMs acting as the reasoning and decision-making layer.
Build Retrieval-Augmented Generation (RAG) pipelines, including:
Document ingestion and chunking strategies
Embedding generation and vector storage
Vector search and hybrid retrieval
Implement Knowledge Graph–based solutions, including Graph RAG, to enable reasoning over structured and unstructured enterprise data.
Design and deploy AI systems on cloud infrastructure, with strong experience in AWS Bedrock and related AWS services.
Ensure adherence to SDLC best practices, including design, implementation, testing, deployment, monitoring, and maintenance.
Write clean, modular, well-documented code and participate in code reviews and architectural discussions.
Required Skills & Qualifications
Strong development experience in Python and/or TypeScript.
Hands-on experience building MCP servers or equivalent LLM integration layers.
Experience with AI agent frameworks (e.g., LangGraph/LangChain, CrewAI etc.).
Prior working experience with AI-powered IDE platforms, such as:
GitHub Copilot
Cursor
Windsurf
Claude Code
Kiro
or similar tools available in the market
Experience designing and deploying AI/LLM systems on AWS cloud platform, especially AWS Bedrock.
Strong understanding of LLM architectures, prompt design, tool usage, memory, and orchestration.
Hands-on experience with vector databases, embeddings, and RAG optimization techniques.
Solid foundation in software engineering principles and the software development life cycle (SDLC).
Experience with REST APIs, microservices, and system integration patterns.
Proficiency with Git-based workflows and CI/CD pipelines.
Good-to-Have Skills
Experience with
spec-driven development
or requirements-driven engineering.
Familiarity with observability, evaluation, and monitoring of LLM and agent behavior.
Exposure to AI security, governance, and responsible AI practices.
Additional requirement for AI SDLC Developer for GE digital area.
