Forward Deployed Engineer

Role overview This role leads the design, delivery, and adoption of AI/ML systems directly inside client environments for an applied AI engineering firm. It suits an experienced engineer who can own systems end-to-end, make architectural decisions under real-world constraints, and drive measurable outcomes at the intersection of technical depth and client partnership.

Responsibilities

- Lead the design and implementation of complex ML/AI systems end-to-end within client environments, owning architecture decisions and driving solutions from research through production.

- Build, deploy, and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development, often within existing client constraints.

- Diagnose and unblock integration challenges in live systems, adapting quickly to unfamiliar codebases, cloud environments, and organizational contexts.

- Embed with client teams, translating business problems into AI system designs that work in their environment, and partner with product, engineering, and leadership on both sides.

- Translate complex AI tradeoffs, risks, and opportunities into clear narratives for technical and non-technical stakeholders, and lead design reviews.

- Drive post-delivery client adoption of AI systems, identify gaps in understanding, build enablement materials, and upskill client engineers.

- Mentor and grow junior and mid-level engineers through coaching, code reviews, and pairing on hard problems.

Requirements

- 7+ years of professional software engineering experience, with 4+ years focused on AI/ML systems in production and deep hands-on generative AI experience.

- Expert software engineering background in Python or similar, with strong design sensibilities for scalable, maintainable systems.

- Deep expertise with cloud platforms, especially AWS services and AWS GenAI offerings.

- Proven track record designing and shipping complex agentic systems in production, including within client or enterprise constraints.

- Mastery of AI frameworks and orchestration tools, plus strong experience with evaluation frameworks and observability tools for LLM applications.

- Deep understanding of AI safety, responsible AI principles, prompt injection defenses, and PII handling.

- Extensive experience building RAG pipelines, including chunking strategies, embedding models, vector databases, and advanced retrieval techniques.

- API design experience, advanced cost optimization expertise, and strong working knowledge of Docker and Kubernetes.

- Demonstrable, day-to-day usage and expert knowledge of AI-forward coding tools such as Claude Code and Cursor.

Benefits and work setup

- Salary range of $177,375-$209,625 USD.