Technical Team Lead

Compunnel, Inc.; Harrisburg (Dauphin County); On-site; USD 120,000 - 150,000; Full time

Job summary

A technology solutions company seeks a Technical Team Lead to drive AI/ML initiatives within its Center of Excellence. This individual will define technical frameworks and reference architectures, ensuring effective adoption across the organization. Candidates should have substantial experience in ML architecture, AWS, and Databricks ecosystems. Excellent communication and MLOps expertise are crucial for success in this role.

Qualifications

• 8–12+ years of experience in data/ML platform engineering or ML architecture.
• Minimum 3 years of experience designing solutions on AWS and Databricks at enterprise scale.
• Proven experience in defining reference architectures and reusable accelerators.

Responsibilities

• Own the technical capability roadmap for the AI/ML CoE.
• Design and maintain end-to-end reference architectures on AWS and Databricks.
• Lead cross-functional workshops and develop documentation for upskilling.

Skills

MLOps expertise

Solution design on AWS

Excellent communication skills

Reference architecture definition

Experience with GenAI patterns

Tools

AWS

Databricks

MLflow

Terraform

Job description

Overview

We are seeking a highly experienced Technical Team Lead to serve as the AI/ML Technical Capability Owner within our evolving AI Center of Excellence (CoE).

This role is pivotal in democratizing AI/ML across the organization by defining technical frameworks, reference architectures, and persona-approved toolsets on AWS and Databricks.

The ideal candidate will bridge enterprise architecture, data science, security, and business units to enable scalable, secure, and impactful AI/ML adoption.

Key Responsibilities

• Own the technical capability roadmap for the AI/ML CoE, aligning with business outcomes, governance, funding, and adoption plans.
• Translate organizational goals into technical guardrails, accelerators, and standardized delivery paths.

Reference Architectures & Frameworks

• Design and maintain end-to-end reference architectures for batch/streaming, feature stores, model training/serving, GenAI (RAG, Agentic AI) on AWS and Databricks.
• Publish reusable blueprints including modules, templates, starter repositories, and CI/CD pipelines tailored to various personas (e.g., Data Scientist, ML Engineer, Software Engineer).

Tools & Platforms

• Curate and evaluate tools across data, ML, GenAI, and MLOps (e.g., Databricks Lakehouse, MLflow, AWS S3, Lambda, Bedrock, Unity Catalog).
• Conduct vendor assessments and define selection criteria, SLAs, and total cost of ownership models.
• Define technical guardrails for data security, lineage, access control, PII handling, and model risk management in alignment with AI policies.
• Establish standards for experiment tracking, model registry, approvals, monitoring, and incident response.

Enablement & Community Building

• Lead cross-functional workshops, engineering guilds, and “train-the-trainer” programs.
• Develop documentation, hands-on labs, and internal training courses to upskill teams.

Delivery Acceleration

• Partner with platform and product teams to implement shared services such as feature stores, model registries, and inference gateways.
• Advise solution teams on architecture reviews and unblock complex programs.
• Present technical roadmaps and deep-dive sessions to executives and engineering communities.
• Showcase ROI and adoption success through demos, KPIs, and case studies.

Required Qualifications

• 8–12+ years of experience in data/ML platform engineering or ML architecture.
• Minimum 3 years of experience designing solutions on AWS and Databricks at enterprise scale.
• Proven experience in defining reference architectures, golden paths, and reusable accelerators.
• Strong MLOps expertise including MLflow, CI/CD, feature stores, model serving, and observability.
• Experience with GenAI patterns (RAG, vector search, prompt orchestration, safety/guardrails).
• Security-by-design mindset with knowledge of IAM/KMS, data classification, and compliance frameworks.
• Demonstrated ability to lead large technical communities and influence without authority.
• Excellent communication and presentation skills for both technical and executive audiences.

Preferred Qualifications

• AWS and Databricks certifications (e.g., Solutions Architect, Machine Learning Specialty).
• Experience with Kubernetes/EKS, Terraform, Delta Live Tables, and Unity Catalog policies.
• Background in manufacturing, industrial IoT, or edge computing.

Success Metrics (First 12 Months)

• Adoption: 70% of AI/ML initiatives using CoE golden paths and approved tooling.
• Time-to-Value: 30–50% reduction in time to first production model or GenAI workload.
• Quality & Risk: 90% compliance with model governance controls; reduced incidents.
• Enablement: 4+ reusable blueprints, 2+ shared services in production, 6+ enablement sessions per quarter.

30/60/90 Day Plan

• 30 Days: Inventory current tools and initiatives; draft capability heatmap and initial reference architecture; publish near-term guardrails.
• 90 Days: Launch shared services (feature store, model registry, evaluation harness); formalize governance checks; publish KPI dashboard and FY26 roadmap.