AI Systems & Applications Engineer

A hands-on engineering role focused on building production-grade AI/ML systems and the automation infrastructure that supports them - driving AI adoption into developer workflows, internal tooling, and domain-specific applications across the organization.

Description
As a member of the AI Infrastructure & Applications team, you will lead the design, development, and production deployment of AI/ML-powered systems alongside the automation infrastructure and developer platforms that support them.
You will architect intelligent, scalable solutions used across the organization - driving AI adoption into developer and automation workflows, internal tooling, and domain-specific applications - while also building and maintaining the automation frameworks and infrastructure those systems depend on.
The systems you build are expected to be production-grade, reliable, observable, and continuously improving.","responsibilities":"Architect and ship end-to-end AI-powered applications and pipelines, from prototype to production.
Build agentic systems, RAG pipelines, and tool-use patterns that integrate LLMs into real workflows.
Define and own AI quality metrics (accuracy, groundedness, hallucination rate, task completion) and integrate them into CI/CD release gates.
Design evaluation frameworks for non-deterministic systems: offline evals, human-in-the-loop review, and automated regression suites.
Harness AI/LLMs to extend and enhance existing automation infrastructure, improving system performance and operational efficiency.
Build scalable automation frameworks, APIs, and tooling used across the organization.
Collaborate with engineering, CI, and domain teams to address automation needs across hardware, software, and cloud.
Distill requirements from a large, diverse user base into generic, reusable, maintainable solutions.
Implement monitoring, drift detection, and structured feedback pipelines for continuous improvement.
Apply rigorous engineering discipline - test design, release criteria, rollback strategies - to AI-native deployments.
Partner with product, design, and domain experts to define use cases, acceptance criteria, and rollout plans.

Preferred Qualifications
Experience leading projects end-to-end - from initial scoping and stakeholder alignment through delivery- coordinating across engineering, product, design, and domain teams.
Practical systems management experience: configuration management, dependency resolution, and deployment tooling across cloud and on-prem environments.
Ability to design sustainable automation systems serving a large, diverse engineering user base.
Hands-on experience with orchestration frameworks and managing the full development lifecycle of complex, multi-component systems.
MA in Computer Science, Software Engineering, or related field.

Minimum Qualifications
BSc in Computer Science, Software Engineering, or related field - or equivalent industry experience.
Strong programming, system design, and API design skills with a focus on scalability and production-readiness.
Proficiency in Python for automation, API development and pipeline engineering.
Experience building automation frameworks, internal developer tools, and shared platforms at scale.
Solid understanding of prompt engineering, retrieval strategies, context management, and model orchestration.
Hands-on experience building and deploying LLM-powered systems: agentic pipelines, RAG, tool-use, and function-calling.
Strong debugging skills across the full AI stack; familiarity with LLM safety and responsible AI practices.
Experience designing and running AI evaluations - automated and human-in-the-loop - and embedding quality gates into CI/CD release workflows.","internalDetails":null