MLOps Lead

Licensed sponsor London Full-time Posted 1 week ago

We are seeking an experienced and commercially minded AI Decisioning & MLOps lead to build and lead a market-leading capability focused on deploying machine learning, AI and decisioning solutions into real-world business environments.

This role will move CACI beyond traditional analytics delivery by operationalising predictive models, AI solutions and proprietary datasets within client systems. You will create scalable decisioning solutions that continuously learn, adapt and improve outcomes across customer acquisition, retention, experience, operations, risk and growth.

Working across consulting, data science and engineering teams, you will help shape and deliver production AI solutions for clients across retail, consumer goods, financial services, utilities, property and public sector markets. Recent internal strategy and proposition development work has highlighted a growing focus on AI decisioning, operationalised analytics and always-on learning systems.

Key Responsibilities

Lead the AI Decisioning & MLOps Strategy
Lead the technical implementation and evolution of CACI's AI Decisioning and MLOps capability.
Design and implement scalable architectures that operationalise analytical outputs into production decisioning systems.
Create standards, frameworks and governance processes for production AI.
Develop reusable assets, accelerators and best practices.

Build Production AI Solutions
Advise on the framework to transform predictive models into enterprise-grade production solutions.
Create deployment frameworks for:
Next Best Action models
Customer propensity models
Churn and retention models
Customer lifetime value models
Forecasting and optimisation models
Geospatial and location intelligence models
Implement model monitoring, retraining and lifecycle management processes.

Connect Analytics to Business Outcomes
Design solutions that integrate AI into client workflows and operations.
Advise on the framework that connects models and decision engines into:
CRM platforms
Customer Data Platforms
Marketing automation platforms
Contact centres
Websites and digital channels
Business operating systems
Ensure analytical outputs drive measurable action and value.

Experience & Skills

Essential

Production grade Python experience
Considerable experience using AWS
Sagemaker Unified Studio, MLflow, Inference endpoints
Model monitoring / drift detection (e.g. SageMaker Model Monitor, Evidently, WhyLabs)
CI/CD for ML (SageMaker Pipelines, GitHub Actions/GitLab CI, or Azure DevOps)
Containerization & orchestration (Docker, Kubernetes/EKS)
Feature store (SageMaker Feature Store, Databricks Feature Store, or Tecton)
Infrastructure as Code (Terraform or CloudFormation/SAM)
A/B testing / experimentation frameworks
Data privacy/governance awareness (GDPR, consent management)
Experience deploying and operationalising generative AI solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows and real-time inference architectures in enterprise environments.
Strong understanding of AI, machine learning operations and decisioning ecosystems.
Experience working in multidisciplinary teams across analytics, technology and consulting.
Track record of delivering commercial value through data and AI.
Experience engaging senior stakeholders and translating technical concepts into business outcomes.
Strong understanding of data engineering, cloud platforms and modern data architectures.

Desirable

Experience with technologies and platforms such as:
Databricks
Snowflake
SageMaker
Pega
Customer decisioning platforms
Experience within one or more of the following sectors:
Retail
Consumer Goods
Financial Services
Utilities
Property
Public Sector

Personal Attributes

You'll be successful in this role if you are:
Commercially minded and outcome-focused
Passionate about AI and emerging technologies
Comfortable navigating both strategic and technical conversations
Naturally collaborative and influential
Curious, innovative and pragmatic
Energised by building new capabilities and scaling teams
Motivated by delivering measurable impact for clients

Success Measures

Within the first 24 months, success will include

Establishing production-grade AI deployment standards and frameworks across CACI engagements.
Delivering multiple production AI solutions for clients.
Increasing adoption of production AI solutions across client engagements.
Creating reusable deployment frameworks and accelerators.
Embedding AI-driven decisioning into client operations.
Strengthening CACI's position as a leader in data-driven decisioning and operational AI.