AI Engineering Lead
Indexed description
We are seeking a hands-on Engineering Manager / AI Architect to lead and grow our Agentic AI development practice. This is a builder-first role: you will spend the majority of your time architecting and coding production AI agent systems while providing technical leadership, mentorship, and light team management to a small, high-impact engineering team.
You will be at the forefront of designing and deploying multi-agent systems that reason, plan, use tools, and execute complex workflows autonomously. You bring deep expertise in LLM-based application development, a strong engineering foundation, and the interpersonal skills to align cross-functional stakeholders around a coherent AI architecture vision.
Job Responsibilities
- Design and implement end-to-end agentic AI systems, including multi-agent orchestration, tool use, memory management, and reasoning pipelines for production-grade LLM applications.
- Evaluate emerging agent frameworks and tools; make informed technology selection decisions with documented trade-offs.
- Prototype novel agent capabilities quickly and iterate from proof-of-concept to production-ready implementations.
- Define and enforce observability best practices: tracing, logging, and evaluation frameworks for agent behavior monitoring in production.
- Partner with business users and project managers to translate business requirements into scalable AI agent architectures.
- Lead and mentor a team of 3–8 AI/ML engineers; conduct code reviews, provide architectural guidance, and foster a culture of engineering excellence.
- 7+ years of hands-on software engineering experience, with at least 2 years focused on LLM application development or AI systems engineering.
- Production experience building AI agents or multi-agent systems using agent frameworks and evaluating LLM.
- Hands-on experience with RAG architectures, vector databases, embedding strategies, MCP tools and agentic AI systems.
- Strong Python engineering skills; comfortable owning and shipping backend services, APIs, and pipelines from design to production.
- Strong system design instincts: ability to reason about latency, reliability, cost, and scalability trade-offs for LLM-powered applications.
- Clear communicator: can explain complex AI architectures to both engineers and business stakeholders.
- People management experience: prior experience leading or mentoring a team of engineers (even informally as a tech lead).
- Experience with fine-tuning or RLHF workflows for domain-specific LLM adaptation.
- Integrity: Tell the truth. We do not brag. We do not make commitments lightly. Once we make a commitment, we devote ourselves completely to meeting that commitment.
- Commitment: Employees are dedicated to the company, view the company’s success as their own and work diligently to make their best contributions. As commitment is mutual, the Company strives to serve the best interests of its employees.
- Innovation: Innovation is the wellspring of the Company's growth. It means more than new ideas; it means putting ideas into practice.
- Customer Trust: We strive to build deep and enduring relationships with our customers, who trust and rely on us to be part of their success over the long term.
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