AI Software Engineer

Job Description

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.

Qualifications

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 Information

Additional requirement for AI SDLC Developer for GE digital area.