Architect, build, and optimize agentic AI systems, including autonomous agents, multi-agent frameworks, and LLM-powered components
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
Evaluate emerging agentic AI models, frameworks, and toolkits
Build POCs and prototypes to benchmark scalability, reliability, and architecture fit
Propose optimal designs based on research and trade-off analysis
Collaborate with product teams, architects, and clients to define requirements and shape AI solutions
Communicate complex technical concepts clearly to non-technical stakeholders
Guide clients on AI tool adoption for both greenfield and brownfield projects

3–5+ years as a Software Engineer, including 1 year of extensive AI Agentic Systems experience
Strong practical development experience — JavaScript/TypeScript preferred; Python, Java, Go also acceptable
Hands-on experience implementing agentic AI architectures, AI orchestration frameworks, autonomous agents & multi-agent systems, MCP integrations
Solid understanding of 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 and experimentation mindset
Experience running POCs, technical evaluations, or benchmarking approaches WILL BE A PLUS
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

AI Engineer

Key skills