AI Lead / Engineering Manager

New

UsersnapB2B SaaS

We're 100% remote. You can work from wherever you like, whenever you like.Full-TimeManager

Salary not disclosed

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Job Details

Experience: 7+ years of software engineering experience; ideally 2-3 years of people leadership.
Required Skills: DockerSQLCI/CDPrompt EngineeringLangChain

Have 7+ years of software engineering experience.
Have shipped AI features to production and owned their lifecycle from problem framing through post-launch iteration.
Bring hands-on technical leadership and experience leading delivery for a small team; 2-3 years of people leadership is preferred.
Have strong full-stack fundamentals across frontend, backend, and infrastructure.
Have practical experience with LLM APIs, prompt engineering, RAG, or similar techniques in live systems.
Build logging, monitoring, and tracing into systems and maintain strong test coverage, including evaluation of AI behavior.
Have solid SQL and schema design skills, and the ability to design scalable data pipelines and systems.
Define success metrics, design experiments, run offline and online evaluations, and use results to make decisions.
Have Docker fluency and CI/CD experience, and follow good practices for versioning prompts, models, and datasets.
Have experience integrating multiple LLM providers, working with vector databases, and using LangChain or similar; be comfortable fine-tuning when appropriate.
Have security and compliance experience or a strong interest in these areas.

Coordinate the small engineering team, including delivery timelines, workload allocation, and project follow-through.
Break down ambiguous goals, sequence work with Product, flag slippage early, and keep commitments on track.
Give technical feedback in code reviews, design discussions, and 1:1s, and help grow the team's capabilities.
Assess engineering practices and establish standards for observability, testing, security, architecture, and timeboxing.
Steward architecture decisions, including model selection and integration approaches, while weighing quality, cost, and latency.
Identify technical risks, particularly around AI reliability, cost, and edge-case behavior.
Design, build, test, monitor, and ship AI-powered features from prototype through production.
Work with LLMs, embeddings, and related AI tooling to address product problems.
Partner with Product on requirements and priorities, and with company leadership on technical planning, resourcing, and sequencing.

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