Back
Highly specialized agentic AI skills and niche toolset reduce applicant density despite metro location.
Specialized agentic AI, LLM orchestration, and evaluation expertise limits cross-industry transferability.
Multiple mandatory technical requirements (6+ years, agentic AI, LLM frameworks, production engineering) create strict shortlisting.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and development of production-grade Agentic AI systems that perform complex, multi-step engineering tasks within the software development lifecycle (SDLC).
Drive adoption and establish best practices for Agentic Engineering workflows including context management, tool use, agent orchestration, evaluation (Evals), and autonomous task execution to improve developer productivity.
Provide technical leadership, architecture oversight, and mentor teams on AI-assisted software development using agent frameworks, integrating with APIs, databases, and cloud environments.
6+ years of software engineering or AI engineering experience with strong hands-on development and production-grade application delivery.
Demonstrated experience building and operating production-grade Agentic AI systems beyond simple chatbots, including understanding of multi-agent architectures, planning, reasoning, context management, tool use, and evaluation frameworks.
Proficient in Python and modern backend/API development; experience with LLMs, agent orchestration frameworks (e.g., LangChain, LangGraph), RAG, and AI coding agent tools such as Claude Code, PI, or Hermes Agent.
Experience with cloud, containers (Docker, Kubernetes), CI/CD, APIs, databases, and production deployments; strong understanding of software architecture, debugging, testing, scalability, and production engineering practices.
Experienced software/AI engineer with deep practical knowledge of Agentic AI systems integrated into the SDLC as engineering tools, not just AI application developers.
Strong technical leadership capability demonstrated by influencing architecture decisions, establishing best practices, and mentoring engineers in AI-assisted development workflows.
Comfortable working at the intersection of AI research, software engineering, and product development with a focus on building scalable, reliable, production-ready agentic systems and developer productivity tools.