Senior Data Platform & AI Lead

Their Corporate Performance Improvement practice is looking for a Senior Data Platform & AI Lead to drive large-scale data platform modernization and AI enablement for clients operating complex, distributed data environments, including federal and defense environments with classified and cross-boundary data. This role requires someone who has designed and implemented enterprise data and AI platforms at scale - not just operated existing pipelines and models. Workplace Type: 80% travel. Hybrid. Employment Type: Full-Time Clearance: Active Secret Clearance required; TS/SCI eligibility strongly preferred. Compensation: Hourly rate is DOE. Experience Should Include: • Modern data platform architecture, including data lakes, lakehouses, and cloud data warehousing (Snowflake, Databricks, BigQuery, Redshift). • Data pipeline and ETL/ELT design, orchestration tooling, and data integration across structured and unstructured sources. • AI/ML platform engineering, including MLOps, model deployment pipelines, feature stores, and LLM/GenAI platform integration. • Data governance, lineage, cataloging, and metadata management frameworks. • Cloud data infrastructure across AWS, Azure, and/or GCP, including cost modeling and platform consolidation. • Data security, privacy, and regulatory compliance. Direct experience with DoD data and cloud policy (for example, the DoD Data Strategy and cloud impact levels IL4 to IL6) is a strong advantage and preferred for this role; GDPR, CCPA, or industry-specific data regulation can substitute. • AI-driven data engineering. Baseline competency with AI-assisted development, testing, and monitoring tools is expected; candidates should articulate how AI accelerates platform build and data operations. • Negotiating with data and AI platform vendors and general understanding of commercial models and licensing structures, including competing vendors and avoiding sole-source dependence. • Designing and architecting highly available, scalable data platforms with proven experience in real-time and batch processing, disaster recovery, and multi-region redundancy. • Federal contracting experience. • Sovereign AI hosting, including running LLMs and the data they access inside controlled boundaries, with consideration for high-density GPU compute and low-latency networking needs. • Data classification and cross-boundary or cross-domain data movement, ensuring users and AI agents see only approved data at the appropriate impact levels. • Data ownership, taxonomy, and governed access across a large federated organization, including stewardship roles. • Data mesh/federated data architecture (for example, DataMesh) that collects and connects data from multiple Military Departments (MilDeps), while keeping domain ownership with each MilDep and enforcing common standards, catalogs, and access policies. Responsibilities: • Lead data platform architecture and engineering assessments, including current-state analysis, target-state design, and modernization roadmaps. • Design AI/ML enablement architectures that support scalable model development, deployment, and monitoring across hybrid and multi-cloud environments. • Assess data governance and AI readiness and develop implementation roadmaps aligned with compliance and business timelines. • Evaluate dependencies between data platform modernization and AI/ML initiative sequencing. • Assess AI infrastructure readiness, including compute, power, networking, and hosting boundaries, alongside the data center and network leads. • Define the data foundation scope, including ownership, taxonomy, governed access, and integrated reporting, and identify where in-flight programs can be accelerated. This includes the federated data model (DataMesh) for collecting and connecting data across MilDeps, covering domain data products, interoperability standards, and cross-MilDep access. • Define and integrate findings into A&M's broader engagement scope and deliverables. • Support practice development through internal training, content creation, and people development. Qualifications: • 10+ years of professional experience in data platform architecture and/or AI/ML engineering for complex, large enterprises, with consulting or advisory delivery experience. • Demonstrated experience leading data platform modernization, AI/ML enablement, or data-driven transformation programs. • Government or defense experience strongly preferred. Regulated industry experience in financial services or healthcare can substitute; deep transformation experience in other complex environments may also substitute. • Bachelor's degree required; an advanced degree in a technical field is a plus. Cloud data or ML certifications (AWS, Azure, GCP, Databricks) are strongly preferred. • Comfortable with travel up to 80%. • Active Secret Clearance required; TS/SCI eligibility strongly preferred. • U.S. Citizenship and Residency required. What Distinguishes a Great Candidate: Has designed and implemented large-scale, highly complex data and AI platform transformations - not just operated existing pipelines and models. Understands the dependency between data platform readiness and AI/ML program success. Can work across data engineering, AI/ML, security, and business stakeholder teams. Brings AI-assisted platform engineering into the delivery toolkit as standard practice. Has worked with classified or cross-boundary data and understands how it constrains platform and AI design.