AI/ML Engineer

Key Requirements

• 3+ years as a software engineer and/or at least 2+ years of substantial production work on AI/ML or GenAI systems.
• Deep understanding of modern AI systems: LLMs, embeddings, vector search, retrieval pipelines, agentic architectures, evaluation frameworks, and production observability for AI.
• Working knowledge of classical ML fundamentals: supervised learning, evaluation metrics, overfitting, data distribution shifts. You know when a transformer isn't the right tool.
• Strong engineering fundamentals in Python (primary), ideally with working comfort in TypeScript/Node.js.
• Proven ability to design and own evaluation methodology, not just use existing frameworks. You can tell signal from noise in a messy eval.
• Can reason about model behaviour at depth: why a system failed, how to measure improvement, how to build systems that degrade gracefully.
• Comfortable at the boundary between research and production. You read papers, assess techniques critically, and decide what's worth adopting.
• High agency and technical leadership. You make calls, communicate trade-offs clearly, and bring others along.
• Pragmatic about when AI is the right tool, and when it isn't. You'd rather ship a simpler non-AI solution when that's genuinely the better answer.
• Low ego, high standards. You hold the bar on technical rigour without slowing everyone down.

Nice-to-Have

• Hands-on experience with fine-tuning, LoRA/QLoRA, DPO, or similar adaptation techniques, with a clear sense of when fine-tuning is worth the cost.
• Has built evaluation harnesses from scratch: LLM-as-judge pipelines, regression suites, red-teaming setups, or human-in-the-loop eval workflows.
• Experience deploying AI systems under serious latency or cost constraints.
• Contributions to or deep familiarity with the open-source ML ecosystem (HuggingFace, vLLM, LangGraph internals, MCP, agent protocols).
• Experience with the full LLMOps lifecycle: model routing, inference serving, caching, monitoring for drift, cost attribution.
• Can discuss specific papers (Chinchilla, ReAct, DPO, Toolformer, etc.) with informed opinions, not just summaries.
• Has worked on classical ML problems (recommendation, forecasting, NLP pre-LLM) and can speak to what changes with GenAI and what doesn't.
• Active contributor to the AI tooling or research community (papers, open-source, blog posts, talks).
• Has shipped real products using AI-assisted coding workflows and can speak to how it changes the rhythm of deep AI/ML work.