Full Stack AI Engineer

Key Responsibilities

Overview This Staff AI Engineer role sits at the intersection of AI systems architecture and full-stack product engineering. The individual will serve as the technical lead for an organization's agentic AI platform, owning the design, deployment, and reliability of LLM-powered features and AI infrastructure across the product ecosystem.

The role focuses on three core areas: building and scaling agent platforms (orchestration, RAG pipelines, memory, routing, and guardrails), delivering production-grade AI features from backend services through user interfaces, and partnering closely with business stakeholders to translate requirements into impactful AI capabilities. This is a hands‑on engineering position centered on production systems rather than research or experimentation.

The engineer will also help establish architectural standards, mentor other AI engineers, influence technical strategy, and represent the AI function across cross‑functional teams. Ideal candidates are experienced builders who can operate independently, make technical decisions, and drive AI initiatives from concept to scale.

Agent Platform & AI Systems

• Architect AI agents and shared platform capabilities including orchestration, memory, tool usage, routing, and workflow management.
• Design and implement RAG pipelines, semantic search capabilities, and retrieval architectures over structured and unstructured enterprise data.
• Establish guardrails, evaluation frameworks, observability standards, and human oversight processes for AI systems.
• Optimize model selection, prompting strategies, tool usage, latency, and cost efficiency at scale.
• Integrate proprietary and open‑source AI models into customer‑facing products and workflows.

Stakeholder Collaboration

• Partner with product, business, and operational stakeholders to define and prioritize AI opportunities.
• Conduct discovery sessions to validate user needs and ensure solutions address real‑world problems.
• Translate business requirements into technical specifications while communicating tradeoffs around scope, accuracy, cost, and performance.
• Align cross‑functional teams on timelines, deliverables, and success metrics.
• Continuously gather feedback and iterate on AI solutions.

Production Engineering

• Develop and deploy end‑to‑end AI‑powered product features, including conversational interfaces, copilots, and workflow automation tools.
• Maintain highly available, observable, and scalable AI services in production environments.
• Support CI/CD processes, testing strategies, and engineering best practices.
• Collaborate with data engineering teams to leverage internal data pipelines, third‑party integrations, and event‑driven architectures.

Technical Leadership

• Drive architecture decisions for AI applications and platform services.
• Define reusable frameworks, tooling, and development standards for AI engineering teams.
• Mentor engineers and improve engineering quality through design reviews, code reviews, and evaluation practices.
• Communicate technical decisions effectively to engineering, product, and executive audiences.
• Represent AI engineering in cross‑functional planning and strategic discussions.

Qualifications

• 6+ years of software engineering experience building and scaling production systems.
• 3+ years of hands‑on experience developing and maintaining production AI, generative AI, or LLM‑powered applications.
• Demonstrated ownership of complex platforms, services, or system architectures.
• Strong backend development experience with Python and/or Node.js.
• Experience with modern frontend technologies such as React or Next.js.
• Expertise in RAG architectures, vector databases, embeddings, semantic search, and retrieval optimization.
• Experience designing evaluation frameworks for AI systems, including quality assessment, hallucination detection, and regression testing.
• Strong API design and development experience.
• Advanced SQL skills and experience working with relational databases such as PostgreSQL.
• Experience with Docker, Kubernetes, cloud infrastructure, and CI/CD pipelines.
• Familiarity with modern AI development tools and workflows.
• Comfortable operating in ambiguous, fast‑paced environments with a high degree of ownership and autonomy.

Compensation

• Base salary range: $190,000 - $210,000
• Compensation is dependent on experience, technical expertise, and overall fit for the position.