AI/ML Technical Capability Owner

Req number: R6344

Employment type: Full time

Worksite flexibility: Remote Who we are

CAI is a global technology services firm with over 8,500 associates worldwide and a yearly revenue of $1 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary We are seeking a highly skilled AI/ML Technical Capability Owner — Center of Excellence (COE) to define our technical "golden paths," reference architectures, and persona-approved toolsets across AWS and Databricks. You'll be the connective tissue between enterprise architecture, data science, security, and business units, designing frameworks, enabling scaled adoption, and presenting compellingly to audiences from engineering guilds to executives. You will be democratizing AI/ML for technical users, giving developers the tools, frameworks, guidance, and trainings to develop AI/ML solutions on their own. The AI/ML Technical Capability Owner will also measure value of technical tools and products developed on those tools. This position will be full-time and remote.

Job Description

What You'll Do

Strategy & Ownership: Own the technical capability roadmap for the AI/ML CoE; understand technical user needs on AI capabilities, align with the Business Capability Owner on outcomes, funding, chargeback model, governance, and adoption plans. Translate company goals into technical guardrails, accelerators, and "opinionated defaults" for AI/ML delivery.

Reference Architectures & Frameworks: Design and maintain end-to-end reference architectures on AWS and Databricks (batch/streaming, feature stores, training/serving, RAG/GenAI, Agentic AI). Publish reusable blueprints (modules, templates, starter repos, CICD pipelines) and define golden paths for each persona (Data Scientist, ML Engineer, Data Engineer, Analytics Engineer, Software Engineer, TE Citizen AI/ML Developer).

Personal-Approved Tools & Platforms: Curate the best-fit suite of tools across data, ML, GenAI, and MLOps/LMMOps (e.g., Databricks Lakehouse, Unity Catalog, MLflow, Feature Store, Model Serving; AWS S3, EKS/ECS, Lambda, Step Functions, CloudWatch, IAM/KMS; Bedrock for GenAI; vector tech as appropriate). Run evaluations/POCs and vendor assessments; set selection criteria, SLAs, and TCO models.

Governance, Risk & Compliance: Define technical guardrails for data security (Structured and Unstructured Data), lineage, access control, PII handling, and model risk management in accordance with TE's AI policy. Identifying enhancements or improvements to TE's AI Policy based on user feedback. Establish standards for experiment tracking, model registry, approvals, monitoring, and incident response.

Enablement & Community: Lead large cross-functional workshops; organize engineering guilds, office hours, and "train-the-trainer" programs. Create documentation, hands-on labs, and internal courses to upskill teams on the golden paths.

Delivery Acceleration: Partner with platform and product teams to stand up shared services (feature store, model registry, inference gateways, evaluation harnesses). Advise solution teams on architecture reviews; unblock complex programs and ensure alignment to standards.

Evangelism & Communication: Present roadmaps and deep-dive tech talks to execs and engineering communities; produce clear decision memos and design docs. Showcase ROI and adoption wins through demos, KPIs, and case studies.

What You'll Need

8–12+ years in data/ML platform engineering, ML architecture, or similar; 3+ years designing on AWS and Databricks at enterprise scale.

Proven experience defining reference architectures , golden paths, and reusable accelerators.

Strong MLOps experience: experiment tracking (MLflow), CI/CD for ML, feature stores, model serving, observability (data & model), drift/quality, A/B or shadow testing.

GenAI experience: RAG patterns, vector search, prompt orchestration, safety/guardrails, evaluation.

Security-by-design mindset (IAM/KMS, network segmentation, data classification, secrets, compliance frameworks).

Track record organizing large groups (guilds, communities of practice, multi-team workshops) and influencing without authority.

Excellent presenter and communicator to both technical and executive audiences.

Nice-to-have:

AWS certifications (e.g., Solutions Architect, Machine Learning Specialty); Databricks Lakehouse/ML certifications.

Experience with Kubernetes/EKS, IaC (Terraform), Delta Live Tables/Workflows, Unity Catalog policies.

Background in manufacturing/industrial IoT/edge helpful.

Physical Demands

This role involves mostly sedentary work, with occasional movement around the office to attend meetings, etc.

Ability to perform repetitive tasks on a computer, using a mouse, keyboard, and monitor.

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to or – 8111.