Lead AI Engineer

● 6+ years of software engineering experience, including strong backend or platform engineering depth.

● 3+ years of hands-on experience building AI, ML, or LLM-powered systems in production environments.

● Proven experience shipping agentic workflows, copilots, intelligent assistants, or AI automation systems that solved real business problems.

● Strong knowledge of LLM orchestration, prompt and context design, tool calling, structured outputs, retrieval-augmented generation, and memory patterns.

● Experience integrating complex systems across APIs, databases, internal tools, cloud services, and business applications.

● Deep understanding of production software engineering, including APIs, distributed systems, asynchronous processing, queues, CI/CD, cloud infrastructure, and service reliability.

● Experience designing evaluation frameworks, testing strategies, and observability for AI systems.

● Strong understanding of AI safety, model failure modes, prompt security, permissions, and governance for sensitive workflows.

● Ability to operate independently in a startup environment with high ambiguity and fast execution cycles.

● Excellent communication and stakeholder-management skills across technical and non-technical teams.

Preferred Qualifications: 

● Experience building multi-agent systems or workflow orchestration layers for complex, multi-step tasks.

● Experience with vector databases, semantic search, ranking systems, knowledge graphs, or enterprise search.

● Familiarity with AI developer platforms, agent frameworks, or internal AI tooling platforms.

● Experience with MLOps, model serving, fine-tuning workflows, or evaluation pipelines.

● Prior experience hiring, mentoring, or leading high-performing engineering teams.

● Startup or scale-up experience where speed, judgment, and resourcefulness were critical.

Core Technical Skills

● Python, Javascript, and at least one production backend language such as TypeScript, Go, Rust, or Java.

● LLM APIs, agent runtimes, orchestration patterns, and structured tool-calling systems.

● Retrieval systems, embeddings, knowledge grounding, and context management.

● API design, workflow engines, cloud deployment, containers, queues, and infrastructure automation.

● Tracing, logging, observability, cost monitoring, and production diagnostics for AI applications.

● Security patterns for AI systems, including permissions, approval gates, and sensitive action controls.

Leadership Traits

● Strong judgment on what should and should not be automated.

● Ability to translate business goals into systems with measurable ROI.

● High ownership, startup speed, and a bias toward shipping reliable outcomes.

● Technical credibility with engineers and strategic clarity with founders.

● Ability to create standards, mentor talent, and build a durable AI engineering culture.

● Partner with founders and business leaders to identify the highest-value workflows to automate, augment, or redesign using AI.

● Design and build production systems using LLMs, tools, retrieval, memory, orchestration logic, guardrails, and human-in-the-loop checkpoints.

● Decide when a deterministic workflow, classic automation, or full agentic workflow is the right technical approach.

● Create reusable internal frameworks and platform components so AI workflows can be launched quickly without becoming one-off prototypes.

● Build reliable tool interfaces and integrations for internal systems, external APIs, databases, SaaS products, and business platforms.

● Own quality and reliability through evaluation harnesses, test datasets, trace inspection, workflow monitoring, alerting, and rollback strategies.

● Design for safety, privacy, security, access control, auditability, and prompt-injection resistance from day one.

● Optimize systems across latency, cost, accuracy, operational resilience, and user experience.

● Mentor engineers, influence architecture across the company, and establish the bar for applied AI engineering.

● Hire and onboard future AI engineers, platform engineers, and adjacent technical talent as the company scales.