AI / LM Specialist with architectures on AWS and Databrick

Job Title: AI/ML Technical Capability Owner Center of Excellence (CoE)

Location: Remote USA / Canada

Duration: Long Term

AI/ML Technical Capability Owner Center of Excellence (CoE)

Reports to: AI/ML Sr. Manager and TE AI COE
Org: Data Insights & Analytics / AI Center of Excellence

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, AI/ML Developer).

Persona-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 bring

Must-have

• 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.