ai engineer

devops engineer

platform engineer

analytics engineer

senior platform engineer

ml ops engineer

Baringa

<div>
 
 About Baringa
 
 
 
 Baringa is a global consulting firm that partners with leaders to drive change and create value. With deep industry expertise, and enabled by advanced technology, the firm helps clients to deliver with greater confidence and certainty. With over 2,000 people across the UK, Europe, North America, Asia and Australia, the firm combines global insight with local understanding.
 
 
 The firm works across energy and resources, financial services, government and public sector, consumer products and retail, pharmaceuticals and life sciences, manufacturing, and technology, media and telecoms, with capabilities spanning strategy, transformation and operational excellence &ndash; all powered by advanced technology, data, AI and digital innovation.
 
 
 Clients value Baringa&rsquo;s collaborative approach and the way its teams integrate seamlessly &ndash; all working with a shared understanding of what matters most. The firm is known for its kind, curious experts who listen closely and care deeply about client success as they help clients transform energy markets, modernise financial platforms, expand telecoms and digital networks through advanced data analytics, enable digital services in government, and unlock growth in consumer sectors.
 
 
 Certified as a
 
 Great Place to Work
 
 around the world, Baringa has been recognised by the
 
 Financial Times
 
 in 22 categories of its UK Leading Management Consultants rankings, and by
 
 Forbes
 
 for four consecutive years as one of the World&rsquo;s Best Management Consulting Firms.
 
 
 
 Our Solutions and AI Lab Team are looking for experienced Machine Learning Engineers to join the team.
 
 
 
 In SAIL, we build state-of-the-art AI solutions that help our clients with some of their biggest projects - ranging from tools that support energy networks forecast risk and adapt to climate change using empirically-derived resilience models, to image recognition software using satellite and aerial imagery, to genAI-powered applications including bespoke assistants and agents.
 
 
 We are focused on delivering value-adding solutions aligned to our client&rsquo;s specific needs. This expertise is applied across clients in all of our industry market sectors (Financial Services, Products &amp; Services, Energy &amp; Resources, Pharmaceutical &amp; Lifesciences and Government).
 
 
 Curious what that impact looks like? Check out our ENA AI Platform case study to see how we accelerated low-carbon device roll-outs for the UK.
 
 
 
 What You Will Be Doing
 
 
 
 <ul>
 <li>
 Defining and implementing machine learning projects over the full lifecycle, from conception to data preparation, model engineering, evaluation and deployment and finally model monitoring and maintenance
 </li>
 <li>
 Establishing and developing ML Ops frameworks and standards for clients and embedding within their infrastructure
 </li>
 <li>
 Working with clients to take them on the journey, upskilling along the way and ensuring they are kept in the loop and can take ownership after you roll off the project
 </li>
 <li>
 Performing maturity assessments across clients&rsquo; Cloud/AI environments and recommending improvements
 </li>
 <li>
 Building ML strategy blueprints and advising clients on the different technology options
 </li>
 <li>
 Translating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organisations strategy, policies and standards and in some cases, help customers to define new policies, philosophies and standards
 </li>
 <li>
 Helping clients to identify risks and mitigations for their ML and DS programmes, as well as transition from on-prem to modern cloud-based infrastructures (AWS, Azure, GCP)
 </li>
 <li>
 Working with clients in key areas of ML model governance, such as in defining philosophies including fairness, transparency, interpretability, and accountability
 
 
 </li>
 </ul>
 
 Your Skills And Experience
 
 
 
 We are seeking passionate and dynamic ML engineers who are excited by building production ML solutions, and keen to take an active part in the growth of the company. We&rsquo;re looking for people who can both advise our clients and get hands on in technical delivery to bring a solution to life.
 
 
 <ul>
 <li>
 Passionate person who is excited by problems within machine learning and can bring a good mix of technical delivery and core consulting skills in client engagements
 </li>
 <li>
 Advanced degree in computer science, mathematics, physics, engineering or related STEM field
 </li>
 <li>
 Strong problem-solving skills and solid grounding in classical ML and deep learning: from applied statistics and traditional machine learning algorithms to transformers and SOTA deep learning
 </li>
 <li>
 Excellent collaboration and communication skills, both with teams and in client-facing engagements
 </li>
 <li>
 Interested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agents
 </li>
 <li>
 Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries (e.g. Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environments
 </li>
 <li>
 Ability to design, deploy and maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API design
 </li>
 <li>
 Hands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (e.g., AWS Sagemaker, Azure Machine Learning studio)
 </li>
 <li>
 Be a &lsquo;lifelong learner&rsquo; and can demonstrate a drive to always be learning and developing your skillsets and develop the skillsets of others around you
 
 
 </li>
 </ul>
 Join us
 
 
 All applications received will be reviewed by a member of our Talent Acquisition team. We never rely solely on automated screening or AI tools to make hiring decisions. Your application will be considered for employment without regard to race, ethnicity, religion, gender, gender identity or expression, sexual orientation, nationality, disability, age, faith or social background. We do not filter applications by university background and encourage those who have taken alternative educational and career paths to apply. We would like to actively encourage applications from those who identify with less represented and minority groups. We operate an inclusive recruitment process, ensuring reasonable adjustments where needed. Please contact a member of our Recruitment Team to discuss further.
 
 
 Baringa Privacy Notices
 
 
 
 For UK &amp; EU
 
 
 
 Your personal data will be retained by Baringa for up to two years, in accordance with our UK Recruitment Privacy Notice / EU Recruitment Privacy Notice, to evaluate your application and meet our legal and reporting obligations. In line with the General Data Protection Regulation (GDPR), you have the right to request access to, rectification, or erasure (subject to legal limitations) of your personal data. For more information, please contact us at privacy@baringa.com
 
 
 
 For the USA
 
 
 
 Your personal data may be retained by Baringa for up to two years, as outlined in our Recruitment Privacy Notice (AMER &amp; APAC), to support the recruitment process and internal reporting requirements. Where applicable, and in accordance with relevant federal and state laws, you may have the right to request access to or correction of your personal information. For further details, please contact privacy@baringa.com
 
 
 
 For Australia &amp; Singapore
 
 
 
 Your personal data will be retained by Baringa for up to two years, in accordance with our Recruitment Privacy Notice (AMER &amp; APAC), to assess your application and meet applicable reporting and legal obligations. In line with the Australian Privacy Act and Singapore&rsquo;s Personal Data Protection Act (PDPA), you may have rights to access, correct, or request limited deletion of your personal data. For more information, please contact us at privacy@baringa.com
</div>

Machine Learning Engineer

JPMorganChase

<div>JOB DESCRIPTION 
 
 
Payments sit at the center of the global economy, connecting businesses and consumers around the world. You will join a team driving innovation in this fast evolving space as new technologies reshape how money moves. In this role, you will work at the forefront of modern machine learning and generative AI to deliver meaningful, lasting impact on global finance. You will partner closely with product, operations, risk, and technology teams to turn ideas into measurable outcomes. You will also have opportunities to mentor others and help shape how we build and operate production AI. 
 
As a Vice President Machine Learning Data Scientist in Payments Machine Learning, you will lead the design, development, and production deployment of machine learning applications, including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable in production. You will mentor engineers and data scientists and translate complex technical work into clear decisions and outcomes for stakeholders.
Job Responsibilities<ul><li>
Lead end-to-end delivery of machine learning and AI solutions for complex payments and banking operations problems, from discovery and framing to production rollout and lifecycle management</li><li>
Develop innovative machine learning solutions, including generative AI and multi-agent approaches, and define evaluation, safety, and monitoring strategies for production use</li><li>
Own production deployment patterns including containerization, continuous integration and delivery, automated testing, model and prompt registries, model and version governance, monitoring and alerting, and rollback strategies</li><li>
Architect and deploy scalable, reliable, and secure machine learning and large language model services integrated with strategic platforms and downstream consumers across APIs, batch, streaming, and event-driven patterns, meeting service level objectives</li><li>
Partner with product, operations, risk and control, and technology teams to influence roadmaps, align on requirements, and deliver data-led transformations</li><li>
Establish reusable, modular data science and machine learning capabilities that scale across use cases, including feature engineering, evaluation harnesses, prompt tooling patterns, agent frameworks, orchestration, and context and memory management</li><li>
Provide technical leadership and mentorship through code reviews, design reviews, best practices, and upskilling across data science and engineering partners</li><li>
Communicate with technical and non-technical stakeholders, translating model outputs into decisions, tradeoffs, and operational plans</li><li>
Maintain strong documentation for approaches, model cards, runbooks, and operational procedures</li></ul>
Required Qualifications, Capabilities, and Skills<ul><li>
Master's degree in a quantitative field, or equivalent practical experience</li><li>
Deep understanding of machine learning fundamentals with strong applied data analysis skills</li><li>
Demonstrated experience designing rigorous evaluation and measurement in real-world settings</li><li>
Demonstrated experience deploying and operating machine learning models in production at scale, including monitoring, drift and performance management, reliability, incident management, and continuous improvement</li><li>
Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging</li><li>
Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing</li><li>
Ability to design intrinsic and extrinsic evaluations aligned with business goals, including offline and online alignment and guardrails for unintended outcomes</li><li>
Experience working in regulated environments with awareness of model risk, controls, privacy and security, and audit-ready documentation</li><li>
Strong stakeholder management and teamwork skills, with the ability to drive outcomes in partnership with cross-functional teams</li></ul>
Preferred Qualifications, Capabilities, and Skills<ul><li>
Experience with NLP and generative AI, including large language models, retrieval-augmented generation, tool and function calling, agentic workflows, multi-agent orchestration, and related evaluation and safety patterns</li><li>
Familiarity with agentic building blocks and standards, including orchestration frameworks, context and memory management, and interoperability protocols such as MCP</li><li>
Experience with machine learning frameworks and data science packages such as PyTorch, TensorFlow, scikit-learn, NumPy, pandas, SciPy, and statsmodels</li><li>
Experience deploying to AWS, including services such as SageMaker and Bedrock, and operating production large language model and machine learning workloads with attention to cost, latency, performance, security, and scaling</li><li>
Experience integrating human-in-the-loop and user feedback signals into iterative improvement, including active learning, preference signals, and labeling strategies</li></ul>
ABOUT US
 
 
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. 
<div><div>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. 
</div></div> 
 
ABOUT THE TEAM
 
 
J.P. Morgan's Commercial &amp; Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial &amp; Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.</div>

Applied AI ML Data Scientist, Vice President - Payments

Anaplan

<div><div>At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.

What unites Anaplanners across teams and geographies is our collective commitment to our customers' success and to our Winning Culture.

Our customers rank among the who's who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.

Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins &ndash; big and small.

Supported by operating principles of being strategy-led, values-based and disciplined in execution, you'll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let's build what's next - together!</div>
Role Overview

We are seeking a Platform Lead to spearhead our ML Ops infrastructure, cost-optimisation, and deployment strategies. You will manage a talented team of DevOps engineers while remaining deeply technical and hands-on. Your primary mission is to build, scale, and secure the foundational platforms for our machine learning (ML) and generative AI (GenAI) models while maintaining financial accountability. 
 
Your Impact
<ul>
<li>Team Leadership &amp; Collaboration: Lead and manage a dedicated DevOps team, mentoring both junior and senior engineers while collaborating closely with Data Science and Engineering leaders.</li>
<li>Infrastructure Strategy &amp; Automation: Define the infrastructure roadmap for AI/ML workloads and automate provisioning across cloud environments using Infrastructure as Code (IaC).</li>
<li>MLOps &amp; LLMOps Engineering: Architect, maintain, and optimise robust MLOps/LLMOps pipelines and CI/CD frameworks for continuous model deployment.</li>
<li>GenAI Production Deployment: Deploy Large Language Models (LLMs) into production environments, ensuring high availability, low latency, and optimal performance for GenAI applications.</li>
<li>FinOps &amp; Budget Management: Establish FinOps frameworks to track, allocate, and forecast AI infrastructure spend, managing high-cost GPU/CPU cloud budgets.</li>
<li>Resource Efficiency &amp; Unit Economics: Implement auto-scaling, spot instances, and down-scaling policies to eliminate waste, while providing full visibility into the unit economics of training and serving LLM models.</li>
<li>Observability &amp; Incident Response: Establish 24/7 incident response, telemetry, and observability metrics to monitor system performance, model drift, and data pipelines.</li>
<li>Data Governance &amp; Security: Enforce strict data governance, platform security, and compliance protocols across all AI/ML infrastructure.</li>
</ul>
Your Skills
<ul>
<li>Extensive production experience deploying and supporting ML systems.</li>
<li>Proven track record of leading engineering teams.</li>
<li>Demonstrated experience with Generative AI and LLM deployment patterns.</li>
<li>A proven history of reducing cloud spend on large-scale AI clusters.</li>
<li>Experience with tools like MLflow, Kubeflow, LangSmith, or Phoenix.</li>
<li>Expertise in AWS/GCP/Azure cost tools, Kubecost, or Cloudability.</li>
<li>Extensive background of Kubernetes (K8s), Docker, and service meshes.</li>
<li>Expert knowledge of Terraform, Ansible, Jenkins, or GitHub Actions.</li>
<li>Proficient in Python, Bash, or Go.</li>
<li>Familiarity with Triton Inference Server, vLLM, or Hugging Face TGI.</li>
</ul><div>
Our Commitment to Diversity, Equity, Inclusion and Belonging (DEIB)
We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren't just words on paper &ndash; this is what drives our innovation, it's how we connect, and it contributes to what makes us a market leader. We believe in a hiring and working environment where all people are respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or any other aspect which makes people unique. We hire you for who you are, and we want you to bring your authentic self to work every day!

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive equitable benefits and all privileges of employment. Please contact us to request accommodation.

Fraud Recruitment Disclaimer

It has come to our attention that fraudulent and fictitious job opportunities are being circulated on the Internet. Prospective candidates are being contacted by certain individuals, mainly through telephone calls, emails and correspondence, claiming they are representatives of Anaplan. The main purpose of these correspondences and announcements is to obtain privileged information from individuals.

Anaplan does not:
<ul>
<li>Extend offers to candidates without an extensive interview process with a member of our recruitment team and a hiring manager via video or in person.</li>
<li>Send job offers via email. All offers are first extended verbally by a member of our internal recruitment team whenever possible and then followed up via written communication.</li>
</ul>
All emails from Anaplan would come from an @anaplan.com email address. Should you have any doubts about the authenticity of an email, letter or telephone communication purportedly from, for, or on behalf of Anaplan, please send an email to people@anaplan.com before taking any further action in relation to the correspondence.

Candidate data processed during our recruitment activities is handled in accordance with our Candidate Privacy Notice. This may include the use of artificial intelligence or automated tools to assist our team in evaluating qualifications.</div></div>

Platform Lead - ML Ops

Brave

<div>
 
 
 AI Systems Researcher / Research Engineer
 
 
 
 
 UK: London (Preferred) - Hybrid
 
 
 
 
 
 
 
 About Brave
 
 
 
 Brave is on a mission to protect the human right to privacy online. We&rsquo;ve built a free web browser that blocks creepy ads and trackers by default, a private search engine with a
 
 truly
 
 independent index, private AI assistant, latest privacy and security features, and a private ad network (opt-in!) that directly rewards you for your attention. Already over 120 million people regularly use Brave for a faster, more private web. Millions more switch every month.
 
 
 
 
 
 The internet is a sea of ads, hackers, and echo chambers. Big Tech makes huge profits off our data, and tells us what&rsquo;s true and what&rsquo;s not. Brave is fighting back. Join us!
 
 
 
 
 
 
 Summary
 
 
 
 Join us at Brave as we redefine the future of online experiences with a range of cutting-edge, top-performing products. We&rsquo;re seeking an experienced AI Systems and Performance Researcher/Engineer to shape and improve our performance evaluations and optimizations across desktop and mobile. If you&rsquo;re passionate about innovation, privacy, and delivering impactful AI solutions, this is your opportunity to make a difference. We need you to help with a wide range of key products, ranging from our browser ML models, novel applications, on-device AI agents, performance evaluations, and collaborating closely with privacy and security researchers to protect the users from AI risks.
 
 
 
 
 
 
 About you
 
 
 
 An ideal candidate will have a mix of skills that include the following:
 
 <ul>
 <li>
 Demonstrates a high level of initiative and consistently delivers high-quality solutions
 </li>
 <li>
 Has an enquiring mind and a disciplined scientific approach to extracting facts and understanding observed behavior
 </li>
 <li>
 Is excited by the potential of realizing high-value commercial outcomes and change the way that the advertising business operates
 </li>
 <li>
 Want to be part of a high-growth startup company with global ambitions
 </li>
 <li>
 Experience with software development practices (Python, C++, JavaScript and/or Rust)
 </li>
 <li>
 Has a proven track record implementing data driven products and a broad understanding of the state of the art in systems and networks
 </li>
 <li>
 Comfortable working in an open-source setting
 </li>
 <li>
 Able to create and deploy mobile/desktop applications and systems
 </li>
 <li>
 Has a passion for helping protect users&rsquo; privacy and security
 </li>
 <li>
 A Ph.D. in computer science is highly preferred, but we may consider some exceptions with industry experience
 </li>
 </ul>
 
 
 
 
 
 Specific Skills
 
 
 <ul>
 <li>
 Background and/or previous work in AI systems research and performance evaluation.
 </li>
 <li>
 Expertise in optimizing and evaluating ML models and/or privacy &amp; security technologies for on-device performance.
 </li>
 <li>
 Experience with modern Machine Learning (ML) frameworks (e.g., Pytorch, TensorFlow, MLX, ONNX Runtime, Core ML, TensorRT, etc.).
 </li>
 </ul>
 
 
 
 
 
 Skills That Will Set You Apart from the Competition
 
 
 <ul>
 <li>
 ML Systems (pipelines, performance evaluation, etc.)
 </li>
 <li>
 Open source projects on mobile/desktop systems optimization
 </li>
 <li>
 Experience with confidential computing and security/privacy protocols
 </li>
 <li>
 Ph.D. in computer science from a top-tier school
 </li>
 <li>
 A strong publication record in premier systems, performance, or ML
 </li>
 <li>
 Work experience in industry is desirable
 </li>
 </ul>
 
 
 
 
 
 Working at Brave - What we offer
 
 
 <ul>
 <li>
 Ability to work at an exciting and well-funded international startup headquartered in San Francisco
 </li>
 <li>
 Cutting-edge technology, allowing you to work with state-of-the-art tools and software solutions
 </li>
 <li>
 Competitive compensation and plenty of room for personal growth
 </li>
 <li>
 Great international exposure and team atmosphere
 </li>
 </ul>
 
 
 
 
 We are looking to hire in London but would
 
 consider
 
 an exceptional candidate in the rest of Europe.
 
 
 
 
 
 NO AGENCIES or RECRUITERS PLEASE - Unfortunately, we cannot work with third parties on these searches.
 
 
 
 
</div>

AI Systems Researcher/Research Engineer

International SOS

<div>
 
 About The Role
 
 
 
 We are looking for an experienced Senior Machine Learning Engineer to join our Product team, in Chiswick, West London,
 
 
 You will be responsible for building production-grade AI capabilities across the platform&rsquo;s generative and agentic paradigms &ndash; from retrieval-augmented knowledge assistants and context-aware responses to multi-step agent workflows. The role combines strong machine learning and software engineering skills to deliver grounded, governed, and scalable solutions that move from Lab prototype to Factory production.
 
 
 This is an excellent opportunity for an experienced engineer, looking for their next move in a global organization.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and implement ML, generative, and agentic AI solutions &mdash; RAG pipelines, prompt workflows, tool-calling agents, and predictive models
 </li>
 <li>
 Build grounded retrieval over enterprise knowledge with source citation and tenant isolation
 </li>
 <li>
 Integrate models via the model gateway, applying guardrails, PII redaction, and content safety on every request
 </li>
 <li>
 Develop and maintain agent orchestration, memory, and human-in-the-loop escalation paths
 </li>
 <li>
 Perform data preprocessing, feature engineering, prompt design, and evaluation using enterprise datasets
 </li>
 <li>
 Deploy solutions through MLOps/LLMOps pipelines with monitoring, evaluations, and SLAs
 </li>
 <li>
 Optimise models and prompts for accuracy, latency, cost, and groundedness
 </li>
 <li>
 Run experiments, track metrics against golden sets, and iterate to improve quality
 </li>
 <li>
 Collaborate with AIOps and Security to integrate solutions into CI/CD and production monitoring
 </li>
 <li>
 Support responsible-AI practices, model cards, and version control for every release
 
 
 </li>
 </ul>
 
 About You
 
 
 
 6+ years in AI/ML engineering or applied machine learning
 
 
 Strong Python skills with scikit-learn, TensorFlow, PyTorch, or XGBoost, plus experience with LLM frameworks (LangChain/LangGraph) and RAG
 
 
 Experience with cloud AI services (AWS Bedrock/SageMaker, Azure, or GCP) and vector stores
 
 
 Proficiency in SQL and working with data warehouses/lakes and embeddings
 
 
 Familiarity with MLOps/LLMOps, containerisation (Kubernetes), and CI/CD
 
 
 Understanding of prompt engineering, evaluation harnesses, and guardrails
 
 
 Strong grasp of ML theory, software engineering practices, and version control (Git)
 
 
 
 Benefits
 
 
 
 <ul>
 <li>
 Competitive salary and incentive scheme
 </li>
 <li>
 Warm, supportive, and open company culture
 </li>
 <li>
 An opportunity to thrive in a global environment
 </li>
 <li>
 Hybrid working: 3 days in the office
 </li>
 <li>
 Birthday holiday and option to purchase additional annual leave
 </li>
 <li>
 Comprehensive Benefits Package: Private Pension, Private Medical Insurance, Life Assurance and more
 </li>
 </ul>
</div>

Senior Machine Learning Engineer

Sainsbury's

<div>
 We&rsquo;d all like amazing work to do, and real work-life balance. That&rsquo;s waiting for you at Sainsbury&rsquo;s. Think about the scale it takes for us to feed the nation. The level of data, transactions and variety it involves. Then you&rsquo;ll realise that ours is a modern software engineering environment because it has to be. We&rsquo;ve made serious investment into a Tech Academy and into setting standards and principles. We iterate, learn, experiment and push ways of working such as Agile, Scrum and XP. So you can look forward to awesome opportunities in everything from AI to reusable tech.
 
 
 
 Why join us
 
 
 
 At Sainsbury's, we're investing in shared AI and machine learning platforms that enable teams across the business to build, deploy and operate intelligent solutions safely, securely and at scale.
 
 
 You'll help shape the engineering foundations that power machine learning, generative AI and agentic applications across the organisation. Working as part of a platform engineering team, you'll build reusable capabilities that make it easier for product teams, data scientists and engineers to deliver value while meeting our standards for security, reliability, governance and cost efficiency.
 
 
 Our platforms include:
 
 
 <ul>
 <li>
 Azure Machine Learning
 </li>
 <li>
 AI Gateway capabilities built on Azure API Management
 </li>
 <li>
 Shared patterns and tooling for agentic and generative AI solutions
 </li>
 <li>
 Core platform services covering identity, networking, observability, security, deployment and cost management
 
 
 </li>
 </ul>
 You don't need to be an AI or machine learning specialist. We're looking for strong engineers with cloud platform, infrastructure or DevOps experience who are excited to apply their skills to the next generation of AI-enabled products and services.
 
 
 
 What You'll Do
 
 
 
 <ul>
 <li>
 Design, build and operate shared platform capabilities that enable teams to develop and deploy ML and AI solutions efficiently and securely
 </li>
 <li>
 Create reusable infrastructure, tooling and automation that improves the developer experience and reduces operational overhead
 </li>
 <li>
 Build and maintain cloud-native platform services covering deployment, networking, security, identity, monitoring and governance
 </li>
 <li>
 Collaborate with product teams, data scientists and ML/AI Ops engineers to understand requirements and provide scalable platform solutions
 </li>
 <li>
 Drive engineering excellence through automation, standardisation, observability and reliability practices
 </li>
 <li>
 Contribute to the evolution of our AI and ML platform strategy, ensuring solutions remain secure, resilient and cost-effective
 </li>
 <li>
 Troubleshoot complex technical issues across cloud services, infrastructure and applications
 </li>
 <li>
 Support and mentor engineers within the team, sharing knowledge and driving continuous improvement
 
 
 </li>
 </ul>
 
 Who You Are
 
 
 
 
 Essential skills and experience
 
 
 
 <ul>
 <li>
 Experience building, operating or improving cloud platforms in AWS, Azure or GCP
 </li>
 <li>
 Strong understanding of infrastructure as code
 </li>
 <li>
 Strong programming skills, with experience developing production-quality solutions in Python or other modern programming languages
 </li>
 <li>
 Experience with CI/CD pipelines, Git-based workflows and modern software delivery practices
 </li>
 <li>
 Knowledge of cloud networking, identity management, security and access controls
 </li>
 <li>
 Experience managing containerised workloads and cloud-hosted application platforms
 </li>
 <li>
 Strong troubleshooting and problem-solving skills across infrastructure and application layers
 </li>
 <li>
 Experience building reusable platform capabilities and self-service engineering solutions
 </li>
 <li>
 Ability to lead technical initiatives and take ownership of delivery outcomes
 </li>
 <li>
 Strong collaboration and communication skills, with a willingness to coach and support others
 </li>
 <li>
 A growth mindset, with the ability to learn new technologies and apply sound engineering principles in unfamiliar domains
 
 
 </li>
 </ul>
 
 Desirable Skills And Experience
 
 
 
 <ul>
 <li>
 Experience with Microsoft Azure platform services
 </li>
 <li>
 Azure Machine Learning
 </li>
 <li>
 Azure API Management
 </li>
 <li>
 Azure OpenAI, Azure AI Foundry or related AI platforms
 </li>
 <li>
 Platform Engineering and Developer Experience practices
 </li>
 <li>
 MLOps, model deployment and model lifecycle management
 </li>
 <li>
 Experience operating platforms in large-scale enterprise environments
 </li>
 <li>
 Knowledge of generative AI architectures, AI gateways, agent frameworks or agentic delivery patterns
 
 
 </li>
 </ul>
 
 What Success Looks Like
 
 
 
 <ul>
 <li>
 Engineers can quickly and safely build and deploy ML and AI solutions using shared platform capabilities
 </li>
 <li>
 Platform services are secure, reliable, observable and cost-effective
 </li>
 <li>
 Reusable patterns and automation reduce duplication across teams
 </li>
 <li>
 Platform adoption grows through a great developer experience and strong engineering partnerships
 </li>
 <li>
 AI and ML workloads can be operated confidently at enterprise scale
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Generous colleague discount across Sainsbury&rsquo;s, Argos, and Habitat
 </li>
 <li>
 Competitive holiday allowance
 </li>
 <li>
 Bonus scheme
 </li>
 <li>
 Pension plan
 </li>
 <li>
 Flexible / smarter working policy
 </li>
 <li>
 Access to special offers on gyms, restaurants, holidays, retail vouchers and more
 
 
 </li>
 </ul>
 We are committed to being a truly inclusive retailer, so you&rsquo;ll be welcomed whoever you are and wherever you work. Around here, there&rsquo;s always the chance to try something new - whether that&rsquo;s as part of an evolving team or somewhere else across the business - and we take development seriously and promise to support you. We also recognise and celebrate colleagues when they go the extra mile and, where possible, offer flexible working. When you join our team, we&rsquo;ll also offer you an amazing range of benefits. Here are some of them:
 
 
 Starting off with colleague discount, you'll be able to get 10% off at Sainsbury's, Argos, TU and Habitat after 4 weeks. This increases to 15% off at Sainsbury&rsquo;s every Friday and Saturday and 15% off at Argos every pay day. We've also got you covered for your future with our pensions scheme and life cover. You'll also be able to share in our success as you may be eligible for a performance-related bonus of up to 10% of salary, depending on how we perform.
 
 
 Your wellbeing is important to us too. You'll receive an annual holiday allowance, and you can buy additional holiday. We also offer other benefits that will help your money go further such as season ticket loans, cycle to work scheme, health cash plans, pay advance (where you can access some of your pay before pay day) as well access to a great range of discounts from hundreds of other retailers. And if you ever need it there is also an employee assistance programme.
 
 
 Moments that matter are as important to us as they are to you which is why we give up to 26 weeks&rsquo; pay for maternity or adoption leave and up to 4 weeks&rsquo; pay for paternity leave.
 
 
 Please see www.sainsburys.jobs for a range of our benefits (note, length of service and eligibility criteria may apply).
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ML / AI Platform Engineer

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 JOB DETAILS
 
 
 
 
 
 Role Title: AI Engineer
 
 
 
 
 
 Must have skills:
 
 
 &bull; Proven experience as an AI, Machine Learning, or Privacy Engineer.
 
 
 &bull; Proficiency in programming languages such as Python is added advantage.
 
 
 &bull; Experience with deep learning frameworks like TensorFlow, PyTorch, or JAX.
 
 
 &bull; Experience working with multi-cloud technologies. GCP is mandatory.
 
 
 &bull; Excellent analytical and communication skills, with the ability to explain complex technical and privacy concepts to non-technical stakeholders
 
 
 &bull; Experience with adversarial testing and robustness evaluation of AI models.
 
 
 &bull; Experience in the end-to-end development of AI/ML models and applications.
 
 
 &bull; Ability to lead and train the team on model training
 
 
 
 
 
 Minimum number of relevant years of experience: 8-10
 
 
 
 
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Artificial Intelligence Engineer

Explore 137+ Ml ops engineer in London this September. Find roles across top companies and apply now to boost your career.