AI-augmented Software Developer

Sigma Software

About The Position

We are looking for a highly skilled AI-augmented Engineer with strong engineering experience and deep hands-on exposure to AI SDLC, autonomous agents, multi‑agent systems, and AI orchestration frameworks. This role focuses on designing, building, and optimizing agentic AI systems — from autonomous agents and MCP integrations to complex orchestration layers for both new products and existing long‑running platforms. The ideal candidate combines strong programming skills (JavaScript/TypeScript preferred, but other languages are welcome) with an ability to work independently, evaluate different approaches, and structure efficient AI development workflows. A consulting mindset is essential — you should be able to advise clients, explain trade-offs, and guide them through adopting modern agentic AI practices.

Requirements

3–5+ years as a Software Engineer, including 1 year of extensive AI SDLC experience
Strong practical development experience — JavaScript/TypeScript preferred, but Python, Java, Go, etc. are also acceptable
Hands-on experience implementing: AI orchestration frameworks, Autonomous agents & multi-agent systems, MCP integrations
Solid understanding of the AI model lifecycle, prompting, evaluation loops, and continuous improvement
Ability to work independently and own end-to-end technical delivery
Consulting experience or strong client-facing communication skills
Curiosity, experimentation mindset, and willingness to build greenfield AI solutions
Experience running POCs, technical evaluations, or benchmarking approaches
Self-driven engineer with strong programming skills
Consulting mindset with ability to advise clients
Passion for experimenting with cutting-edge AI frameworks
Strong communication skills for cross-functional collaboration
Ability to deliver robust, scalable AI solutions

Nice To Haves

Experience with vector databases, RAG, and embeddings
Background in backend engineering, MLOps, or system design
Understanding of LLM fine‑tuning or model customization
Experience integrating AI into production-grade applications

Responsibilities

Design and implement Model Context Protocol (MCP) integrations and agent orchestration layers
Build and maintain AI SDLC pipelines, tooling, and automation logic across the development lifecycle
Implement agentic AI patterns, multi-agent workflows, and intelligent task automation
Work with tools such as Claude Code, Cursor, Microsoft Copilot, and other AI engineering environments
Evaluate emerging agentic AI models, frameworks, and toolkits
Build POCs and prototypes to benchmark scalability, reliability, and architecture fit
Propose optimal designs based on hands-on research and clear analysis of trade-offs
Work with product teams, architects, and clients to define requirements and shape AI SDLC solutions
Communicate complex technical concepts clearly to non-technical stakeholders
Guide clients on AI tool adoption (both greenfield and brownfield)