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MLOPs Lead

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What you’ll work on

Full posting
  • Design scalable architectures that turn model outputs into production decisioning systems

  • Create standards, frameworks and governance processes for production AI

  • Implement model monitoring, retraining and lifecycle management processes

  • Advise on frameworks connecting models and decision engines to client CRM and channels

  • Develop reusable assets, accelerators and best practices

From the employer’s posting
Design and implement scalable architectures that operationalise analytical outputs into production decisioning systems.
Create standards, frameworks and governance processes for production AI.
Implement model monitoring, retraining and lifecycle management processes.
Advise on the framework that connects models and decision engines into:
Develop reusable assets, accelerators and best practices.
Apply at CACI

Posting record

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Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 30, 20265 days ago
Recorded sightings
5
Last seen by us
Nov 3, 2026

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How this job compares

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Day 5

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From 4,284 similar postings that came down. Coming down is not always a hire.

CACI is hiring

6 open jobs

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  • Takes jobs down at a usual pace
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See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • AWS
  • Azure
  • Databricks
  • Docker
  • Kubernetes
  • MLflow
  • SageMaker
  • Snowflake
  • Terraform
Where the posting names them
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:

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

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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.   Why Join Us? This is a rare opportunity to define and build a strategic growth capability within one of the UK's leading data, analytics and insight businesses. You will shape how AI, machine learning and decisioning are delivered across our client base, influence the future direction of C&MI and play a key role in transforming how organisations use data to make decisions. If you are excited by the opportunity to build something new, work with exceptional colleagues and help organisations realise the full potential of AI, we'd love to hear from you.  
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Kensington, London, UK

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