AI Implementation Lead

Required Skills & Experience

- Experience being an AI Implementation Thought Leader within an organization

- Deep hands-on experience with Databricks Lakehouse architecture and implementation.

- Experience defining and architecting AI strategy and implementation frameworks within the Databricks ecosystem

- Establish best practices for AI solution design, governance, and lifecycle management

- Proven implementation experience with: AI/BI Genie (or equivalent conversational analytics patterns), Agent Bricks / autonomous AI agent development within Databricks, MLflow for model tracking, deployment, and lifecycle management

- Guide teams on how to structure AI solutions for enterprise scalability, including: Data pipelines model orchestration, Governance and lineage (Unity Catalog), Model lifecycle and monitoring (MLflow)

- Strong AI/ML delivery history: model development, validation, operationalization, monitoring.

- Strong Python, PySpark, SQL, and data engineering fundamentals (building production pipelines, optimizing performance, ensuring reliability)

- Demonstrated experience delivering AI-driven recommendations, smart notifications, and decision intelligence workflows.

- Partner with leadership to align AI initiatives with organizational priorities and solution goals

- Mentor and influence data engineering and AI teams on best practices and emerging capabilities

Nice to Have Skills & Experience

- Experience supporting executive-facing platforms (e.g., executive dashboards, portals, or decision systems)

- Experience scaling AI capabilities across large transformation programs or multi-phase initiatives

- Familiarity with Agent Bricks or similar agent orchestration frameworks

Job Description

Energy Pulse is seeking a senior AI Implementation Lead (Databricks) to drive the structured adoption and scaling of AI capabilities inside the Databricks Lakehouse. This role is designed for someone who has implemented AI capabilities in production (not just built demos) and can serve as the “been-there-before” technical leader someone who can guide design decisions, set standards, and influence direction across stakeholders. This position is aligned to the AI & Automation Enablement phase, focused on implementing AI/BI Genie, Agent Bricks, advanced analytics/ML/AI, and AI-driven data quality automation in a governed, enterprise-ready way.

What We’re Specifically Looking For (the “strategist” filter):

- You’ve ridden multiple technology adoption waves and can differentiate hype from durable architecture and operating models.

- You can raise the quality bar quickly. Identify what’s off, fix direction early, and coach teams into best practices.

- You’re comfortable influencing executives and cross-functional leadership with clear rationale and outcomes.

- You bring a “builder architect advisor” blend: hands-on enough to execute, senior enough to set direction.

Expectations:

- Own the AI implementation strategy within Databricks: define how AI capabilities should be structured, operationalized, governed, and scaled across the platform.

- Establish “what good looks like”: patterns, reference architectures, guardrails, and best practices so teams build consistently and production-ready (not “one-off” solutions).

- Layer AI capabilities over an existing, moving platform: integrate AI features into ongoing delivery without disrupting execution design for adoption, stability, and measurable outcomes.

- Lead enterprise-grade agentic implementation using Agent Bricks and Databricks-native GenAI/agent tooling, including orchestration patterns, evaluation approaches, and operational readiness.

- Deliver business-facing AI via AI/BI Genie and conversational analytics, ensuring solutions are usable, secure, and governed for broad consumption.

- Drive decision intelligence use cases (recommendations, smart notifications, next-best-action workflows) from concept → production, with clear success metrics.

- Implement AI-driven data quality automation: anomaly detection, intelligent validation, and monitoring patterns embedded into pipelines and Lakehouse assets.

- Own model lifecycle and operationalization using MLflow (tracking, deployment, governance, monitoring) and align to enterprise release practices.

- Be the senior voice in the room: confidently recommend (and diplomatically push back on) suboptimal approaches; guide stakeholders toward scalable patterns internally and with the stakeholders.