JOB SUMMARY:

The Senior AI/ML Engineer role will design and build custom AI-powered applications end-to-end, from prototype to production, focused on delivering measurable business value. The Senior AI/ML Engineer will engineer full AI systems that integrate model capabilities into real products: robust API endpoints, agentic or AI workflow orchestration, scalable backend services, and well-designed data models. Hands-on in implementation, deeply involved in system quality and reliability, and eager to mentor junior engineers while raising the team’s engineering bar.

MAJOR RESPONSIBILITIES:

AI Development

• Architect and implement end-to-end AI solutions, including backend services, APIs, databases, and model integration layers.
• Develop and maintain API endpoints (REST/GraphQL) that reliably serve AI capabilities to internal and external consumers.
• Design and implement backend database schemas (relational and/or NoSQL) and data access patterns optimized for AI workloads and application needs.
• Build scalable AI infrastructure patterns (job queues, caching, streaming, async workers) to support low-latency and high-throughput use cases.
• Apply secure engineering practices including authentication/authorization, secrets management, threat modeling, and PII handling.

Engineer AI & Agentic Workflows

• Implement AI pipelines including Retrieval-Augmented Generation (RAG), tool/function calling, structured output, and agentic workflows.
• Choose appropriate model strategies: prompt-based, RAG, fine-tuning, or hybrid approaches based on cost, latency, and quality requirements.
• Evaluate and integrate foundation models (open-source and commercial) with attention to licensing, security, privacy, and performance.

AI Evaluation

• Define and implement evaluation frameworks to measure AI system quality (accuracy, relevance, grounding, hallucination rates, safety, latency, cost).
• Create automated test suites for prompts/workflows and regression testing for model and data changes.
• Monitor production systems for drift, degradation, and reliability issues; establish observability (logs, traces, metrics) and incident response practices.

Collaboration and Mentorship

• Partner with Product, Data Science, Data Engineering, DevOps and Platform teams to translate requirements into robust technical solutions.
• Mentor junior engineers through code reviews, technical guidance, and best-practice sharing.
• Lead by example in engineering rigor, craftsmanship, and pragmatic decision-making.
• Understands and actively participates in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
• Performs other duties as assigned.

EDUCATION:

• Bachelor’s degree in computer science, Engineering, Data Science, or a related field is required.
• An advanced degree (Masters or PhD) in a relevant field is strongly preferred, emphasizing computer science, machine learning, robotics, or business management.

EXPERIENCE:

• 5+ years in software engineering and/or applied AI/ML roles, with demonstrated delivery of production systems.

ADDITIONAL INFORMATION:

• AI/ML Expertise: Strong working knowledge of modern ML/GenAI tools (e.g., PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex or similar).
• Backend Engineering: Proven ability to build and maintain backend services and APIs (Python/TypeScript/Java/Go or similar).
• Data & Storage: Experience designing database schemas and working with relational and/or NoSQL databases; familiarity with vector databases is a plus.
• Cloud Proficiency: Proficient in at least one major cloud provider (AWS, GCP, or Azure), including deployment and managed services.
• Evaluation Discipline: Demonstrated experience measuring AI system quality with repeatable evaluation methods and production monitoring.
• Scaling Mindset: Ability to design systems for performance, reliability, and maintainability at scale.
• Communication: Strong written and verbal communication skills; able to explain tradeoffs clearly to technical and non-technical stakeholders.
• Mentorship: Eagerness and ability to mentor junior engineers and elevate team practices.

Your talent, skills and experience will be rewarded with a competitive compensation package.

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