Head of AI

Esko, a Veralto company

1d•Remote

About The Position

Headquartered in Broomfield, Colorado, TraceGains, a Veralto company, connects people and information so teams can work smarter. As a global technology company, we provide networked innovation, quality, and compliance solutions to consumer brands that want to reduce supply chain risk, speed up business processes, and take control of data. TraceGains operates as a fully remote company! At TraceGains, we believe “we’re in this together,” and our goal is to provide the most innovative solutions in the industry. We are in the game to change the industry, and with the help of our ever-growing TraceGains Network, we have created the CPG industry’s first networked ingredients marketplace. We also know that innovation and ingenuity start with prioritizing a diverse workforce and a deeply inclusive workplace. We encourage people from all backgrounds to apply to our positions. TraceGains is at a pivotal moment in our AI transformation journey. We’ve proven initial success with Intelligent Document Processing and defined a clear 3-year AI strategy that will revolutionize how the global food and beverage supply chain operates and innovates. We now need an experienced AI leader to build the foundational data, MLOps, algorithms, agents, and infrastructure that will accelerate our AI initiatives and scale our capabilities across the organization. Reporting to the Senior Vice President, Engineering, the Head of AI will lead the strategy, architecture, design, build, and evolution of TraceGains’ next‑generation Agentic AI data platform. The successful candidate will enable self‑service for data science and engineering teams, establish privacy‑first governance and production‑grade MLOps practices, and lay the foundation for scalable, reliable AI delivery across all TraceGains’ products. You'll build and lead a platform team of both data engineers and AI/ML engineers, establishing TraceGains as a leader in AI-driven and agentic supply chain intelligence. A critical part of this role is making sound, budget-conscious platform decisions—explicitly balancing accuracy, latency, reliability, and total cost of ownership as we scale. You will lead technically-grounded AI governance and policy discussions (privacy, security, risk, and compliance) and translate them into practical engineering standards and enforceable controls. You will also establish robust data and evaluation foundations—building scalable dataset pipelines (including synthetic data where appropriate) and repeatable evaluation/benchmarking for both generative AI (RAG/agents) and classical ML.

Requirements

Master's degree in Computer Science, Data Engineering, or related field (or equivalent experience), Ph.D. is a plus.
10+ years building enterprise data and AI platforms in production environments
2-3 years driving LLM adoption for Generative AI use cases
1+ year(s) of building agentic systems including deployment of MCP servers to enable agentic AI.
Proven ability to lead cost-aware AI delivery, technically grounded governance decisions, and large-scale evaluation/data practices.
Proven track record designing and implementing MLOps platforms
Experience with Pipelines, model monitoring, and drift detection
Experience with graph databases, and vector databases to support RAG
Ability to drive CI/CD for ML, security, safety, monitoring, and observability
Proven ability to establish shared platform capabilities that serve multiple product teams
Proven ability to deliver AI capabilities into production
Strong communication skills with ability to present to executive leadership
Track record of cross-functional collaboration with AI product teams, ML, and business stakeholders
Experience establishing technical standards and governance frameworks across distributed teams
Ability to be a team player, willing to grow and change and drive change into the organization in a positive and constructive manner.
Experience mentoring technical teams (data engineers, AI/ML engineers, and platform engineers)
Occasional travel required for department meetings, all company events, in-person seminars/networking events, etc.
Successful completion of a drug and background screening process including, but not limited to, employment verifications, criminal search, OFAC, SS Verification, as well as credit screening, where applicable and in accordance with federal and local regulations.

Nice To Haves

Experience building AI teams from 0 to1
Experience developing and fine-tuning foundational models
Experience with infrastructure automation (IaC) via Terraform or similar technology
Proficient with Docker and Kubernetes
Experience with supply chain, food safety, or regulatory compliance domains
Multi-cloud architecture experience with Azure as primary and AWS/GCP familiarity
Knowledge of LLMs, RAG architectures, and advanced NLP applications
Open source contributions to ML or data platform tools
Experience with knowledge graphs and ontology management
Background in privacy-preserving ML techniques and federated learning

Responsibilities

Architect a scalable, multi-tenant agentic AI data platform using the Azure technology stack.
Design a hybrid data architecture supporting operational systems, agentic AI workloads, and a knowledge graph
Build infrastructure utilizing vector and graph databases for RAG applications and semantic search
Design and implement comprehensive MLOps platform including deployment management, AI security and safety, and observability on Azure supporting the full ML lifecycle from experimentation to production.
Build automated pipelines using Azure technologies for continuous integration and deployment
Implement real-time inference infrastructure with monitoring, alerting, and automated drift detection
End-to-end lifecycle management including hydration from existing taxonomies/ontologies
Develop high-performance graph query services and APIs for real-time access to supply chain relationships
Deploy automated validation, conflict resolution, and data quality monitoring to ensure graph consistency and accuracy
Implement Infrastructure as Code (IaC) and build CI/CD pipelines for data products and ML models
Set and enforce platform standards for cost control, model/runtime selection, and performance targets (including budgeting, attribution, and optimization for training + inference).
Lead AI governance with Security/Legal/Privacy, turning policy into technical controls (access, auditability, guardrails, retention, and risk management).
Build scalable data + evaluation systems: synthetic dataset generation where appropriate, automated benchmarks, and release quality gates for RAG/agents as well as classical ML.
Create self-service capabilities with comprehensive monitoring and observability
Drive engineering delivery improvements through AI-based code analysis to improve quality, maintainability, and general code health.
Drive engineering efficiency improvements through best practices and adoption of AI-assisted coding tools and capabilities.
Stay on top of new research including cutting edge AI technology.
Democratize AI through TraceGains teams for adoption and deployment across the platform.