Engineer, AI Strategy and Solutions

The Engineer, AI Strategy & Solutions is responsible for designing and building AI-enabled and conventional software systems that integrate with enterprise data platforms, services, and event-driven architectures to deliver measurable business value. This role translates complex business and technical requirements into scalable, production-ready solutions-including APIs, data pipelines, and model serving infrastructure (batch and real-time)-using established platform patterns.

Key responsibilities include implementing AI capabilities such as inference, retrieval-augmented generation (RAG), and evaluation workflows, while ensuring compliance with Responsible AI principles, security standards, and privacy controls throughout the development lifecycle.

The engineer owns the quality of delivered solutions, including comprehensive testing (unit, integration, end-to-end), performance profiling, and observability through logs, metrics, and traces. Participation in on-call rotations and incident response is expected.

This role collaborates closely with cross-functional teams including Product, Data Science, Security, and Infrastructure. It involves contributing to design reviews, authoring clear technical documentation and runbooks, and advancing shared libraries, SDKs, and CI/CD/MLOps practices. #LI-DNI, * AI & Cloud Software Engineering:

• Provides technical support to project teams on the design, development, and delivery of AI-enabled and conventional software, translating requirements into designs and working code while aligning to platform standards and patterns.
• Maintains knowledge of enterprise software/AI standards (architecture, security/privacy, data contracts, responsible AI) and industry best practices.
• Provides support to project teams to design, build, and deploy AI-enabled and conventional systems that meet safety, reliability, performance, and compliance requirements.
• Assists cross-functional teams and studio leadership to deliver global, multi-platform software and AI solutions across web, mobile, console, and edge environments.
• Supports complex delivery across diverse platforms and geographies, aligning technical execution with business goals.
• Cross-functional quality assurance:
• Review internal and vendor deliverables-API/architecture docs, data schemas, model cards, security/privacy checklists, test plans (unit/integration/load/perf), and test results-submit redlines/issues and track to resolution.
• Supports lifecycle technical reviews and readiness gates-requirements and architecture reviews, threat modeling, model card/data lineage checks, test plan definition (offline evals, A/B, load/perf), deployment/go-live approvals.
• Ensures alignment with standards and best practices.
• Applied AI Engineering & Operations:
• Contribute directly to engineering work: build/integrate services & APIs, ETL/streaming data pipelines, model training/inference code and RAG/retrieval flows; author automated tests; participate in operational support/on-call.
• Supports continuous improvement of engineering processes, templates, and tooling (coding standards, shared SDKs/libraries, CI/CD & MLOps pipelines, evaluation/observability, incident response).
• Supports product/production teams with feature breakdown, estimation, and technical risk management.
• Proactively identify dependencies and risks, facilitate resolution, and maintain momentum across complex initiatives. By bridging product vision with engineering execution, this role drives operational clarity and accelerates value realization.
• AI Platform Engineering & Developer Enablement:
• Create and maintain engineering artifacts-design docs, ADRs, runbooks, deployment playbooks, IaC/config.
• Contribute to shared SDKs/templates and CI/CD/MLOps pipelines; ensure portability, maintainability, reliability, observability, operability, and supportability.
• Collaborate with engineers across Associate/Engineer/Senior bands; participate in targeted internal workshops (AI solution patterns, secure coding, platform usage, Responsible AI), elevating overall engineering quality and velocity.
• Ensures that all systems and models are built with integrity, accountability, and resilience from the ground up.
• Supports and models best in class culture, which promotes innovation, collaboration and problem-solving. Inspires and motivates teams by leading with optimism and a solution-oriented approach, drives for results.
• Understand and actively participate in Environmental, Health & Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
• Performs other duties as assigned., * Required: Proficient in Python plus one of C#/Java/Go/TypeScript/C++; experienced with REST/gRPC APIs, microservices, event-driven architecture, and integration with internal/external services. Works with SQL/NoSQL and data pipelines (ETL/ELT, batch/streaming); familiar with Kafka/Kinesis/PubSub or similar. Deploys on AWS/Azure/GCP using containers/Kubernetes, IaC, and CI/CD; applies MLOps basics (model registry/versioning, feature stores, drift detection). Applies security-by-design (authN/Z, secrets, PII handling) and Responsible AI practices (model cards, eval gates); writes maintainable docs and performs effective code reviews.
• Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
• Consistent attendance is a job requirement.

• Bachelor’s degree in a relevant technical field - e.g., Computer Science, Software Engineering, Computer Engineering, Data Science/Analytics, Electrical Engineering (software focus), or Systems Engineering - or equivalent demonstrated skill and experience (e.g., production-software/AI systems, open-source contributions, published work) required; or equivalent combination of education and experience.
• Master’s degree in Computer Science, AI/ML, Data Science, Software Engineering, or a closely related field preferred.
• Graduate coursework or certifications in machine learning, distributed systems/cloud, MLOps, security/privacy, or data engineering are a plus., * 5+ years delivering multi-platform, networked software (web, mobile, services, edge) to production.
• Hands-on AI solutioning (e.g., computer vision, NLP/RAG, anomaly detection, personalization) with training & inference pipelines, evaluation, and monitoring; or equivalent combination of education and experience.

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