Blue Cloud Softech AI/ML Technical Capability Owner

Job description

<p><p><b>Description : </b><br/><br/>AI/ML Technical Capability Owner Center of Excellence (CoE).<br/><br/><b>Role overview : <br/></b><br/>TE Connectivity is evolving from product-by-product AI delivery to an AI Center of Excellence (CoE) model that democratizes AI/ML for both business and technical users in FY26.<br/><br/>Were hiring a Technical Capability Owner to define our technical golden paths, reference architectures, and persona-approved toolsets across AWS and Databricks.<br/><br/>Youll 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.<br/><br/>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.<br/><br/>The AI/ML Technical Capability Owner will also measure value of technical tools and products developed on those tools.<br/><br/><b>What youll do : </b><br/><br/><b>Strategy & Ownership : </b><br/><br/>- 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.<br/><br/>- Translate company goals into technical guardrails, accelerators, and opinionated defaults for AI/ML delivery.<br/><br/><b>Reference Architectures & Frameworks : </b><br/><br/>- Design and maintain end-to-end reference architectures on AWS and Databricks (batch/streaming, feature stores, training/serving, RAG/GenAI, Agentic AI).<br/><br/>- 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).<br/><br/><b>Persona-Approved Tools & Platforms : </b><br/><br/>- 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).<br/><br/>- Run evaluations/POCs and vendor assessments; set selection criteria, SLAs, and TCO models.<br/><br/><b>Governance, Risk & Compliance : </b><br/><br/>- Define technical guardrails for data security (Structured and Unstructured Data), lineage, access control, PII handling, and model risk management in accordance with TEs AI policy.<br/><br/>- Identifying enhancements or improvements to TEs AI Policy based on user feedback.<br/><br/>- Establish standards for experiment tracking, model registry, approvals, monitoring, and incident response.<br/><br/><b>Enablement & Community : </b><br/><br/>- Lead large cross-functional workshops; organize engineering guilds, office hours, and train-the-trainer programs.<br/><br/>- Create documentation, hands-on labs, and internal courses to upskill teams on the golden paths.<br/><br/><b>Delivery Acceleration : </b><br/><br/>- Partner with platform and product teams to stand up shared services (feature store, model registry, inference gateways, evaluation harnesses).<br/><br/>- Advise solution teams on architecture reviews; unblock complex programs and ensure alignment to standards.<br/><br/><b>Evangelism & Communication : </b><br/><br/>- Present roadmaps and deep-dive tech talks to execs and engineering communities; produce clear decision memos and design docs.<br/><br/>- Showcase ROI and adoption wins through demos, KPIs, and case studies.<br/><br/><b>What youll bring : </b><br/><br/><b>Must-have : </b><br/><br/>- 812+ years in data/ML platform engineering, ML architecture, or similar; 3+ years designing on AWS and Databricks at enterprise scale.<br/><br/>- Proven experience defining reference architectures, golden paths, and reusable accelerators.<br/><br/>- Strong MLOps experience : experiment tracking (MLflow), CI/CD for ML, feature stores, model serving, observability (data & model), drift/quality, A/B or shadow testing.<br/><br/>- GenAI experience : RAG patterns, vector search, prompt orchestration, safety/guardrails, evaluation.<br/><br/>- Security-by-design mindset (IAM/KMS, network segmentation, data classification, secrets, compliance frameworks).<br/><br/>- Track record organizing large groups (guilds, communities of practice, multi-team workshops) and influencing without authority.<br/><br/>- Excellent presenter and communicator to both technical and executive audiences.<br/><br/><b>Nice-to-have : </b><br/><br/>- AWS certifications (e.g., Solutions Architect, Machine Learning Specialty); Databricks Lakehouse/ML certifications.<br/><br/>- Experience with Kubernetes/EKS, IaC (Terraform), Delta Live Tables/Workflows, Unity Catalog policies.<br/><br/>- Background in manufacturing/industrial IoT/edge helpful.<br/><br/><b>Success metrics (first 12 months) : </b><br/><br/>- Adoption : ?70% of AI/ML initiatives using CoE golden paths and persona-approved tooling.<br/><br/>- Time-to-value : 3050% reduction in time to first production model or GenAI workload.<br/><br/>- Quality & Risk : ?90% compliance with model governance controls; measurable reduction in incidents.<br/><br/>- Enablement : 4+ reusable blueprints and 2+ shared services in production; 6+ enablement sessions/quarter.<br/><br/><b>30/60/90 plan : </b><br/><br/>- 30 days : Inventory current tools/initiatives; draft capability heatmap and initial reference architecture; publish near-term guardrails.<br/><br/>- 60 days : Deliver first golden path (e.g., Databricks-centric MLOps with MLflow/UC); run 2 enablement workshops; select initial GenAI stack (incl.

Bedrock stance).<br/><br/>- 90 days : Launch shared services (feature store/model registry + eval harness); formalize governance checks; publish KPI dashboard and FY26 roadmap.<br/><br/><b>Posting blurb (short version) : </b><br/><br/>Were building an AI Center of Excellence to democratize AI/ML across TE Connectivity.<br/><br/>As our AI/ML Technical Capability Owner, youll define the architectures, tools, and guardrails that help teams ship reliable ML and GenAI solutions at scale on AWS + Databricks.<br/><br/>If you love creating golden paths, enabling large technical communities, and presenting your vision from deep dives to the boardroom, wed love to talk.<br/><br/></p><br/></p> (ref:hirist.tech)

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Computer Occupations