Senior MLOps Engineer

Intapp
uk remote
1 year ago
Applications closed

Related Jobs

View all jobs

Senior MLOps Engineer

Senior MLOps Engineer - Production ML at Scale

Senior MLOps Platform Engineer — Cloud & Kubernetes

Senior AI MLOps Platform Engineer - Scale Resilient Cloud

Senior Platform Engineer - AI MLOps Oxford, England, United Kingdom

Senior Full-Stack AI/ML Engineer (Production & MLOps)

As a Senior MLOps Engineer, you will play a crucial role in enabling applied AI. Your main focus will be on the design, build, and maintenance of secure, scalable and efficient ML Platform, with a platform as a product mindset, that automates the end-to-end life-cycle for traditional ML models and LLM models, as part of the Cloud platforms engineering (CPE) directorate. CPE’s mission is to enable our Engineering teams to ship value faster, securely, efficiently and reliably.

In this role, you will:

Design and implement robust MLOps and LLMOps pipelines to automate and optimize machine learning model training, testing, deployment, and scaling.

Collaborate with data scientists and software engineers to ensure operational criteria are met before deployment.

Maintain and enhance continuous integration (CI) and continuous deployment (CD) environments for machine learning systems.

Develop tools to improve visibility into the system's operation and to facilitate rapid troubleshooting and debugging.

Foster a culture of continuous improvement by incorporating feedback and lessons learned into future ML deployments.

Lead initiatives to increase the resilience and scalability of ML systems.

What you need:

Bachelor’s degree in computer science, Engineering, Statistics, or a related field.

Experience in software development or data engineering, with at least 3 years focused on MLOps or similar roles.

Proven track record in designing and deploying scalable machine learning systems in production.

Strong programming skills in Python and experience with ML frameworks and tools (e.g., TensorFlow, PyTorch, MLFlow, MetaFlow, vLLM, Kubeflow, Jupyter notebook, Azure ML Studio, Amazon Sagemaker, Apache Spark, Apache Flink).

Expertise in containerization technologies (e.g., Docker, Kubernetes) and automation tools (e.g., Jenkins, GitLab CI).

Excellent problem-solving skills and the ability to work independently or as part of a team.

Bonus if you have:

Experience with data governance and ensuring compliance with data security regulations.

Familiarity with performance tuning of big data technologies.

LLM Model development

What you will gain at Intapp:

Our culture at Intapp emphasizes accountability, responsibility, and growth. We support each other in a positive, open atmosphere that fosters creativity, approachability, and teamwork. We’re committed to creating a modern work environment that’s connected yet flexible, supporting both professional success and work-life balance. In return for your passion, commitment, and collaborative approach, we offer:

Competitive base salary plus variable compensation and equity

Generous paid parental leave, including adoptive leave

Traditional comprehensive benefits, plus:

Generous Paid Time Off

Tuition reimbursement plan

Family Formation benefit offered by Carrot

Wellness programs and benefits provided by Modern Health

Paid volunteer time off and donation matching for the causes you care about

Opportunities for personal growth and professional development supported by a community of talented professionals

An open, collaborative environment where your background and contributions are valued

Experience at a growing public company where you can make an impact and achieve your goals

Open offices and kitchens stocked with beverages and snacks

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.