senior software engineer

ai engineer

data engineer

data scientist

machine learning engineer

senior machine learning engineer

HOMETRACK

<div>Hometrack is redefining the mortgage journey for lenders, brokers, and consumers by delivering market-leading valuation and property data services to the financial, property, and technology industries. Our key commercial and go-to-market segment is in financial services, primarily mortgage lenders, including nine of the top 10 mortgage providers.
At Hometrack, we are looking for an experienced Senior Machine Learning Engineer, to translate POC model code from data science and analytics into robust pipelines and live APIs.
Responsibilities:<ul><li>
Oversee the deployment of machine learning models into production, ensuring they perform well in real-world conditions and monitoring their performance over time.</li><li>
Work closely with data scientists, software engineers, product managers, and stakeholders to integrate machine learning solutions into our products and services.</li><li>
Develop automated workflows for model retraining, testing, and deployment to streamline the machine learning lifecycle.</li><li>
Advocate for and implement best practices in software engineering, including code reviews, continuous integration, and continuous delivery (CI/CD) for machine learning models.</li></ul>
Requirements:<ul><li>
Strong understanding of machine learning applications, development life cycle processes and tools: CI/CD, version control, testing frameworks, MLOps.</li><li>
Strong Python experience and knowledge, with the ability to write stable, scalable and maintainable code.</li><li>
Experience with data science Python libraries such as Sckit-learn, Pandas, NumPy, PyTorch, PySpark, LightGBM.</li><li>
Have worked with a cloud service, such as AWS.</li><li>
Familiarity developing Infrastructure as code (e.g Terraform, cloudformation).</li><li>
Have some experience with data engineering, building data pipelines with PySpark, SQL (E.g in Databricks, Glue) to power machine learning applications.</li><li>
Comfortable working with Docker and containerised applications.</li><li>
Experience leveraging AI native engineering tooling.</li><li>
Passion for building products that meet customer needs and business objectives.</li><li>
Strong sense of responsibility and a track record of delivering high-quality results in a fast-paced environment.</li></ul> 

Benefits 
<ul><li>
Everyday Flex - greater flexibility over where and when you work</li><li>
25 days annual leave + extra days for years of service</li><li>
Day off for your birthday, house move, good deed day, and digital detox day</li><li>
Cycle to work and electric car schemes
</li><li>Free Calm App membership
</li><li>Enhanced Paternity Leave
</li><li>Fertility Treatment Financial Support
</li><li>Group Income Protection and private medical insurance</li><li>
Gym on-site in London &ndash; or membership in regional offices</li><li>
7.5% pension contribution by the company</li><li>
Discretionary annual bonus up to 10% of base salary</li><li>
Talent referral bonus up to &pound;5K</li></ul></div>

Senior Machine Learning Engineer

Onsera Health

<div>
 
 The Challenge
 
 
 Cardiometabolic conditions will impact 80-90% of people throughout their lifetimes, representing the leading cause of death globally and an important risk factor for neurodegenerative diseases and cancer. Sustainably addressing these diseases requires breakthrough therapeutics, AI/ML innovation, and transformative business models that translate clinical outcomes into economic value.
 
 
 The Opportunity
 
 
 This is an opportunity to build the ML and AI foundations of Onsera Health's breakthrough healthcare platform. Backed by Population Health Partners (PHP)&mdash;the proven venture platform behind Metsera and Corsera Health&mdash;we're building the future of population health management.
 
 
 
 As a Senior Machine Learning Engineer, you will sit at the intersection of data science, platform engineering, and production systems.
 
 Your mission is to design and operate the foundations that allow ML, analytics, and agentic AI systems to move safely and reliably from experimentation into regulated healthcare production environments on Google Cloud Platform. You will be the primary interface between Data Science and Platform Engineering, enabling rapid iteration while enforcing production and compliance standards.
 
 
 Responsibilities
 
 <ul>
 <li>
 
 Design and implement Onsera's agentic AI platform
 
 &ndash; architect LLM/agent framework selection, standardized patterns for tools, memory, guardrails, evaluation, and observability
 </li>
 <li>
 
 Define MLOps protocols for agentic systems
 
 &ndash; environment separation, versioning of prompts, tools, and policies, cost controls, rate-limiting, and fail-safe mechanisms
 </li>
 <li>
 
 Bridge data science and platform engineering
 
 &ndash; translate experimentation needs into GCP infrastructure, scalable compute patterns, and reproducible development environments
 </li>
 <li>
 
 Productionize data science code
 
 &ndash; convert research-grade notebooks into tested, modular, production-grade Python services, batch and streaming pipelines, and scheduled workflows
 </li>
 <li>
 
 Build production-grade data pipelines
 
 &ndash; develop idempotent, observable, and cost-efficient pipelines using BigQuery, Airflow, Google Workflows, and Cloud Run
 </li>
 <li>
 
 Implement CI/CD for ML workloads
 
 &ndash; automated validation, monitoring, rollback strategies, and model lifecycle management
 </li>
 <li>
 
 Establish reliability and governance
 
 &ndash; logging, metrics, tracing, data quality checks, and auditability for model decisions, data lineage, and agent actions
 </li>
 </ul>
 
 What We Offer
 
 <ul>
 <li>
 Mission-driven work addressing critical public health and healthcare economics challenges
 </li>
 <li>
 Ground-floor opportunity to build the ML/AI infrastructure for a breakthrough healthcare platform
 </li>
 <li>
 Partnership with a world-class team of industry leaders, innovators, technologists, bioscientists, and clinicians from PHP and beyond
 </li>
 <li>
 A fast-paced, dynamic, and highly collaborative work environment
 </li>
 <li>
 Competitive salary and benefits package, including participation in our equity program
 </li>
 </ul>
 
 
 
 
 Minimum Qualifications
 
 <ul>
 <li>
 5+ years of experience in machine learning engineering, data engineering, or a related field
 </li>
 <li>
 Strong Python engineering skills with production-quality, typed, and tested code
 </li>
 <li>
 Track record productionizing ML models in batch and/or real-time environments
 </li>
 <li>
 Hands-on experience with analytical data warehouses (BigQuery or equivalent) and workflow orchestration (Airflow or similar)
 </li>
 <li>
 Experience deploying ML systems on GCP, including Cloud Run, GCS, and IAM
 </li>
 <li>
 Infrastructure-as-Code experience (Terraform or equivalent)
 </li>
 <li>
 Experience with feature engineering, model lifecycle management, and ML evaluation
 </li>
 <li>
 Demonstrated ability to collaborate across Data Science, Product, and Platform teams
 </li>
 </ul>
 
 Preferred Qualifications
 
 <ul>
 <li>
 Experience with LLMs, agent frameworks, or AI orchestration systems in production
 </li>
 <li>
 Familiarity with prompt management, tool calling, evaluation, and AI safety patterns
 </li>
 <li>
 Healthcare or regulated-industry experience, including familiarity with HIPAA or SOC-2 compliance
 </li>
 <li>
 Experience with claims data, EHR-derived datasets, or real-world evidence
 </li>
 <li>
 Strong written and verbal communication skills with technical and non-technical stakeholders
 </li>
 </ul>
 
 About Onsera Health
 
 
 We are an early-stage health AI company revolutionizing cardiometabolic care and population health economics connected to weight loss. Our unique position within the PHP ecosystem provides:
 
 <ul>
 <li>
 
 Venture track record:
 
 A proven track record of success in building biotech and healthcare companies
 </li>
 <li>
 
 Deep capital:
 
 Funding and resourcing to support deep tech and scientific R&amp;D&mdash;and to drive growth
 </li>
 <li>
 
 Strategic guidance:
 
 Direct access to industry pioneers (including founders of The Medicines Company and Metsera), healthcare experts (including leaders in virtual care, former FDA commissioners), strategists (McKinsey/QuantumBlack alumni), and world-class scientific talent
 </li>
 <li>
 
 Expert network:
 
 Established connections across pharma, payers, and providers
 </li>
 <li>
 
 Startup agility:
 
 Ownership in the venture, founder mentality, and ground-floor impact
 </li>
 </ul>
 
 About PHP
 
 
 Population Health Partners (PHP) is a premier investment firm established in 2020, aimed at transforming health outcomes for large populations. With offices in New York and London, PHP combines financial resources with industry-leading capabilities and technology. The firm's incubation portfolio includes Metsera and Corsera Health.
 
 
 
 Leadership:
 
 Led by industry veterans including Clive Meanwell (The Medicines Company), Chris Cox (The Medicines Company), and Whit Bernard (Metsera), bringing decades of founder experience and operational excellence in biopharma and healthcare, and Roy Berggren, 30+ year McKinsey Healthcare leader.
 
</div>

<div><div><div>Job ID: R0048311</div>
</div><div><div><div><div><div>Location</div>
<div>London, Middlesex</div>
</div></div></div><div></div><div><div><div><div>Hours/week</div>
<div>37.5 hrs/week</div>
</div></div></div><div></div><div><div><div><div>Payrate range</div>
<div>Unknown</div>
</div></div></div></div><div><div>We believe in better. And we make it happen.
Better content. Better products. And better careers.
Working in Tech, Product or Data at Sky is about building the next and the new. From broadband to broadcast, streaming to mobile, SkyQ to Sky Glass, we never stand still. We optimise and innovate.
We turn big ideas into the products, content and services millions of people love.
And we do it all right here at Sky.
What you'll do
 
We are seeking a highly skilled Senior Machine Learning Engineer to advance our personalised recommendation systems by developing efficient, low-latency solutions that serve millions of users globally. The successful candidate will collaborate closely with data scientists, engineers, and product managers to design intelligent content recommendation mechanisms and drive the ongoing advancement of our Machine Learning Platform.
<ul><li>Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.</li>
<li>Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets.</li>
<li>Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.</li>
<li>Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.</li>
<li>Cross-Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.</li>
<li>Research &amp; Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.</li> 
</ul> 
What you'll bring
<ul><li>Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
</li><li>Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). .</li>
<li>Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe).</li>
<li>Experience with high-volume data processing and real-time streaming architectures.</li>
<li>Strong understanding of recommendation system design and personalisation algorithms.</li>
<li>Familiarity with Generative AI and its applications in production settings.</li>
<li>Exceptional communication and analytical problem-solving skills.</li>
<li>Proven successful experience in mentoring less experienced engineers to improve their technical skills</li>
</ul>A Typical Day at the Office
When you come in, you can grab a coffee or a bit of breakfast from one of the many (subsidised) caf&eacute;s or restaurants on site. Settle in at your desk, have a quick look at Slack to see what's happening in the tech communities, then catch up with everyone at the team stand-up. After that, you&rsquo;ll join your team and pick the first task to get cracking on.
At lunchtime, you&rsquo;ve got a few choices: head to The Pavilion for a bite with the team, pop to the onsite gym for a quick workout, or join in with a lunchtime community meetup &ndash; whatever suits you. Once you&rsquo;re back, you&rsquo;ll carry on working with your team on your current feature.
Later in the afternoon, the team might fancy a quick coffee break before wrapping up the day with a team retrospective.
Global OTT Technology
Our team develops and supports market-leading video streaming services, underpinned by state-of-the-art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across Europe. No matter the device, the time or the place, we make sure that our diverse audiences can easily find and enjoy whatever they want to watch, choosing from the world&rsquo;s best entertainment, news and sport.
The rewards
There's one thing people can't stop talking about when it comes to #LifeAtSky: the perks. Here&rsquo;s a taster:
<ul><li>Sky Q, for the TV you love all in one place</li>
<li>The magic of Sky Glass at an exclusive rate</li>
<li>A generous pension package</li>
<li>Private healthcare</li>
<li>Discounted mobile and broadband</li>
<li>A wide range of Sky VIP rewards and experiences</li>
</ul>Inclusion &amp; how you'll work
We are a Disability Confident Employer, and welcome and encourage applications from all candidates. We will look to ensure a fair and consistent experience for all, and will make reasonable adjustments to support you where appropriate. Please flag any adjustments you need to your recruiter as early as you can.
We&rsquo;ve embraced hybrid working and split our time between unique office spaces and the convenience of working from home. You&rsquo;ll find out more about what hybrid working looks like for your role later on in the recruitment process.
Your office space
Osterley
Our Osterley Campus is a 10-minute walk from Syon Lane train station. Or you can hop on one of our free shuttle buses that run to and from Osterley, Gunnersbury, Ealing Broadway and South Ealing tube stations. There are also plenty of bike shelters and showers.
On campus, you&rsquo;ll find 13 subsidised restaurants, cafes, and a Waitrose. You can keep in shape at our subsidised gym, catch the latest shows and movies at our cinema, get your car washed, and even get pampered at our beauty salon.
We'd love to hear from you
Inventive, forward-thinking minds come together to work in Tech, Product and Data at Sky. It&rsquo;s a place where you can explore what if, how far, and what next.
But better doesn&rsquo;t stop at what we do, it&rsquo;s how we do it, too. We embrace each other&rsquo;s differences. We support our community and contribute to a sustainable future for our business and the planet.
If you believe in better, we&rsquo;ll back you all the way.
Just so you know: if your application is successful, we&rsquo;ll ask you to complete a criminal record check. And depending on the role you have applied for and the nature of any convictions you may have, we might have to withdraw the offer.</div></div></div>

Senior Machine Learning Engineer (Recommendation)

Compare The Market

<div>Senior Machine Learning Engineer &ndash; Customer AI
Function: Data &amp; AI Solutions
 
Location: Hybrid, London office 
Curious about what&rsquo;s next?
 
So are we. Join Compare the Market and help to make financial decision making a breeze for millions.
At Compare the Market, we&rsquo;re a purpose-driven business powered by tech and AI. We&rsquo;re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
We&rsquo;ve carved a meerkat-shaped niche and we&rsquo;re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you&rsquo;ll fit right in. 
We&rsquo;d love you to be part of our journey.
Compare the Market is building AI systems that connect directly to how millions of people in the UK find and buy financial products - and as a Senior Machine Learning Engineer, you will play a central role in making those systems production-ready. That means owning the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use: the pipelines, the deployment tooling, the monitoring, and the shared components that make the whole thing work.
This is a hands-on role with real technical scope. You will work closely with data scientists, engineers, and product teams - contributing to architecture decisions, raising engineering standards, and building the reusable tooling that raises the pace and quality of delivery across a fast-moving and expanding set of AI capabilities and systems. 
Some of the great things you'll be doing:
ML Engineering and AI Systems
<ul><li>Own the end-to-end delivery of production machine learning and AI solutions in collaboration with data scientists and product teams
</li><li>Design and build model pipelines for training, validation, and deployment using modern tooling (e.g. MLflow, Kubernetes)
</li><li>Contribute hands-on code to model packaging, deployment, and lifecycle automation
</li><li>Build systems that monitor model performance, drift, reliability and operational health in real time
</li><li>Support both batch and real-time ML workloads depending on use case requirements
</li>Work on emerging AI and LLM-powered capabilities, helping integrate modern AI techniques into production systems where they can deliver real user value 
<li>
</li></ul>Platform and Standards
<ul><li>Help evolve our internal ML and AI platform to support experimentation, governance, and collaboration
</li><li>Define and promote best practices for ML and AI system design, including reproducibility, testing, CI/CD, model and agent observability and evaluation
</li>Develop shared tools and libraries that accelerate safe, efficient, and scalable ML development 
<li>
</li></ul>Collaboration and Technical Leadership
<ul><li>Work closely with data scientists to productionise experimental models and turn prototypes into robust services
</li><li>Act as a technical mentor and code reviewer for other engineers and contributors
</li>Provide technical leadership across ML and AI initiatives, contributing to architecture discussions and design reviews 
<li>
</li></ul>Culture and Innovation
<ul><li>Contribute to a culture of engineering excellence, collaboration, and continuous learning
</li><li>Stay up to date on emerging tools and approaches in MLOps and applied AI, helping evaluate and adopt technologies where appropriate
</li>Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems 
<li>
</li></ul>What we'd like to see from you:
The Essentials
<ul><li>Hands-on experience with LLM-based systems: prompt engineering, RAG, tool use, or orchestration frameworks such as LangGraph or LangChain
</li><li>Familiarity with multi-step AI patterns - building systems where models plan, retrieve information and take sequences of actions
</li><li>Strong experience deploying ML models into production in cloud-native environments
</li><li>Solid software engineering skills in Python, with experience building scalable services, APIs, and production-quality code
</li><li>Experience with modern ML tooling and platforms (e.g. Databricks, MLflow, Airflow, Kubeflow, SageMaker, Vertex AI)
</li><li>Familiarity with CI/CD pipelines and infrastructure-as-code (e.g. Terraform, CloudFormation)
</li><li>Experience building robust, maintainable, and testable ML pipelines and APIs, including batch or real-time model delivery
</li><li>Strong understanding of ML lifecycle challenges - versioning, testing, monitoring, governance
</li>Excellent collaboration and communication skills, with experience working across data science, engineering, and product teams 
<li>
</li></ul>Nice to Have
<ul><li>Active personal use of AI-assisted or agentic coding tools, and an interest in how similar patterns could be applied to automate and accelerate ML engineering workflows - candidates who are exploring this space, even at an early stage, are encouraged to talk about it
</li><li>Experience in financial services, insurance or another regulated sector
</li><li>Experience deploying real-time or streaming ML models (e.g. Kafka, Flink, Spark Structured Streaming)
</li><li>Passion for automation, tooling, and building reusable systems
</li>Interest in responsible AI and ML model governance 
<li>
</li></ul>Why Compare the Market?
We&rsquo;re a business built for pace and performance. Here, you&rsquo;ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
We believe diverse teams make better decisions, and we&rsquo;re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
If you&rsquo;re ready to stretch yourself, raise the bar, and grow with a team that&rsquo;s serious about performance, innovation, and purpose, we&rsquo;d love to hear from you.</div>

Senior Machine Learning Engineer - Customer AI

Hiscox

<div>
 
 Job Type
 
 
 
 Permanent
 
 
 
 Build a brilliant future with Hiscox
 
 
 
 
 Principal Machine Learning Engineer
 
 
 
 
 Location:
 
 London/York
 
 
 
 Why Hiscox London Market
 
 
 
 Hiscox London Market sits at the centre of global specialist insurance, tackling some of the most complex and unusual risks in the world. These are not commoditised problems, they demand deep expertise, strong judgement, and increasingly, sophisticated data and machine learning capabilities.
 
 
 We have a strong track record of putting AI into real production use, from augmenting underwriting decisions to shaping future market standards through partnerships and market‑first innovation. This is an environment where advanced ML systems are expected to operate reliably, safely, and at scale, not remain in experimentation.
 
 
 You&rsquo;ll join a culture that values technical excellence, ownership, and courage, where senior individual contributors are trusted to set direction, challenge thinking, and build platforms that matter. For a Principal Machine Learning Engineer, this is a chance to work on high‑impact ML systems, influence how AI is adopted across the London Market, and help shape the future of insurance.
 
 
 
 Role Purpose
 
 
 
 As a
 
 Principal Machine Learning Engineer (MLE)
 
 , you bring a wealth of experience in building, scaling, and operating production machine learning systems, and use that experience to provide deep technical leadership across machine learning engineering and MLOps.
 
 
 You play a key role in shaping the architectural strategy for production ML systems and the ML Platform, working closely with Data Science, Engineering, and Platform teams to define patterns, standards, and tooling that enable reliable, repeatable delivery at scale. Through hands‑on contribution, design leadership, and technical mentorship, you help teams navigate complex technical decisions and build robust, maintainable systems.
 
 
 A central focus of the role is enabling the organisation to move quickly without sacrificing quality, evolving the ML platform, supporting the transition from experimentation to production, and helping teams adopt modern engineering practices. This includes championing the effective and responsible use of AI‑assisted development tools as part of a broader approach to improving developer experience, system quality, and long‑term sustainability.
 
 
 Success in this role comes from the practical application of deep experience, strong architectural thinking, and the ability to help others build better systems that deliver real business value.
 
 
 
 Key Responsibilities
 
 
 
 
 Technical Leadership &amp; Ownership (Individual Contributor)
 
 
 
 <ul>
 <li>
 Act as the technical lead for Machine Learning Engineering and MLOps across London Market.
 </li>
 <li>
 Technically lead the most complex and business‑critical ML systems, from architectural design through to production operation.
 </li>
 <li>
 Define and evolve production ML patterns and best practices, covering deployment, orchestration, monitoring, retraining, and decommissioning.
 </li>
 <li>
 Lead deep technical decision‑making, balancing scalability, reliability, security, and developer experience.
 </li>
 <li>
 Contribute hands‑on to critical systems, frameworks, and platform components where the complexity or impact demands it.
 </li>
 <li>
 Define best practices and guardrails for the use of AI‑assisted coding tools, ensuring they are used to improve productivity and code quality without compromising maintainability or operational safety.
 </li>
 <li>
 Lead by example in applying AI‑assisted development techniques (e.g. for prototyping, refactoring, and accelerating complex engineering work) with strong engineering judgement.
 
 
 
 </li>
 </ul>
 
 ML Platform Strategy &amp; Build‑Out
 
 
 
 <ul>
 <li>
 Partner closely with Group and Platform teams to design, build, and evolve the ML Platform, ensuring it supports:
 </li>
 <ul>
 <li>
 Reusable and scalable deployment patterns
 </li>
 <li>
 CI/CD for machine learning
 </li>
 <li>
 Full model lifecycle management
 </li>
 <li>
 Monitoring, observability, and alerting
 </li>
 <li>
 Secure and compliant operation
 </li>
 </ul>
 <li>
 Shape platform standards and interfaces that enable consistent ML delivery across squads and value streams.
 </li>
 <li>
 Lead technical spikes and proof‑of‑concepts to evaluate new tools, approaches, and architectural patterns.
 </li>
 <li>
 Influence developer tooling choices so that AI‑assisted coding tools integrate safely with CI/CD, testing, and governance workflows.
 </li>
 <li>
 Ensure the platform enables fast, safe experimentation while supporting robust, long‑lived production systems.
 
 
 </li>
 </ul>
 
 Governance, Reliability &amp; Commercial Impact
 
 
 
 <ul>
 <li>
 Ensure ML systems meet architecture, security, compliance, and operational standards.
 </li>
 <li>
 Define and implement robust frameworks for monitoring technical health, model performance, and commercial impact of ML systems in production.
 </li>
 <li>
 Champion operational excellence across ML services, including monitoring, alerting, incident management, and post‑incident learning.
 </li>
 <li>
 Ensure clear ownership and lifecycle management for models and ML‑backed services.
 </li>
 <li>
 Promote responsible use of automation and AI‑assisted tooling in safety‑critical or regulated contexts.
 
 
 
 </li>
 </ul>
 
 Collaboration &amp; Influence
 
 
 
 <ul>
 <li>
 Work closely with the Data Science team to ensure a smooth and repeatable transition from experimentation to production.
 </li>
 <li>
 Collaborate with software engineers, product managers, and business stakeholders to deliver end‑to‑end ML‑driven solutions.
 </li>
 <li>
 Act as a senior technical voice in design reviews, architecture forums, and strategic discussions.
 </li>
 <li>
 Influence technical direction and standards through expertise, credibility, and collaboration rather than line management.
 
 
 
 </li>
 </ul>
 
 Technical Mentorship &amp; Capability Development
 
 
 
 <ul>
 <li>
 Provide hands‑on technical mentorship to Machine Learning Engineers and Data Scientists.
 </li>
 <li>
 Raise engineering standards by sharing best practices, patterns, and lessons learned from real production systems.
 </li>
 <li>
 Coach teams on the effective and critical use of AI‑assisted coding tools, reinforcing the importance of code review discipline, testing, and long‑term maintainability.
 </li>
 <li>
 Contribute to technical hiring, assessment, and onboarding from a senior engineering perspective.
 </li>
 <li>
 Help shape long‑term capability by identifying gaps in tooling, skills, and platform maturity.
 
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Experience &amp; Background
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
 </li>
 <li>
 Extensive experience as a senior or principal Machine Learning Engineer delivering production ML systems at scale.
 </li>
 <li>
 Proven track record of owning or shaping ML platforms, MLOps frameworks, or critical ML infrastructure.
 </li>
 <li>
 Experience operating in complex, cross‑functional environments (insurance or financial services experience is advantageous but not essential).
 
 
 
 </li>
 </ul>
 
 Technical Expertise
 
 
 
 <ul>
 <li>
 Exceptional Python skills in a machine learning engineering context, with strong software engineering fundamentals (OOP, testing, design patterns).
 </li>
 <li>
 Deep experience building, deploying, and operating production ML systems, including:
 </li>
 <ul>
 <li>
 Online and batch model serving
 </li>
 <li>
 Monitoring, alerting, and observability
 </li>
 <li>
 Retraining and lifecycle management
 </li>
 </ul>
 <li>
 Strong understanding of core data science concepts, sufficient to:
 </li>
 <ul>
 <li>
 Review and challenge modelling approaches
 </li>
 <li>
 Ensure models are production‑ready and correctly evaluated
 </li>
 </ul>
 <li>
 Hands‑on experience with a major cloud platform (AWS, GCP, or Azure), including containerised deployments.
 </li>
 <li>
 Expert knowledge of MLOps and CI/CD, including:
 </li>
 <ul>
 <li>
 Git‑based workflows
 </li>
 <li>
 Infrastructure as Code (e.g. Terraform)
 </li>
 <li>
 Automated testing and deployment pipelines
 </li>
 </ul>
 <li>
 Strong operational mindset, including APIs, logging, monitoring, reliability, and incident response.
 </li>
 <li>
 Working knowledge of SQL and integration of ML services into wider data and application ecosystems.
 </li>
 <li>
 Experience using AI‑assisted coding tools in a production engineering context, with a clear understanding of their benefits, limitations, and risks.
 
 
 </li>
 </ul>
 
 Why Join Us?
 
 
 
 This is an opportunity to shape the future of machine learning engineering at Hiscox, build a high-performing sub-chapter, and influence strategic decisions, while staying close to the craft you love. You&rsquo;ll have the autonomy to set standards, mentor talent, and explore emerging technologies, all within a collaborative and forward-thinking environment.
 
 
 
 Work with amazing people and be part of a unique culture
 
</div>

Principal Machine Learning Engineer

Accenture

<div>YOU ARE
As a Senior AI/ML Computational Scientist, you will design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients, combining custom models with cloud and third-party AI services to deliver production-ready outcomes.
Your spans the full solution lifecycle &mdash; assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and MLOps/LLMOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems.
THE WORK<ul><li>
Formulate real-world problems into practical, efficient, and scalable AI and Machine Learning solutions</li><li>
Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps &amp; MLOps</li><li>
Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including edge device and HPC</li><li>
Engage in research and development of new AI and high-performance compute algorithms, models, and simulations along with their applications to solve complex business problems at client sites</li><li>
Work with large-scale datasets and utilize data preprocessing techniques to ensure high-quality input for training and production</li><li>
Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools</li><li>
Justify the value of model approaches in business problems</li><li>
Collaborate with teams from both business and technical sides, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project goals and integrate into production</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree or equivalent</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experience as a machine learning engineer or scientist, deploying models in production at scale , including monitoring, alerting, automatic bug filing and auditing.</li><li>
Experience in applying theoretical foundations of computer science, including computer system architecture, system engineering, and programming</li><li>
Experience in distributed computing systems and architecture that may include big data, high-performance compute, engineering simulations, scientific compute, grid and cloud computing, distributed networks</li><li>
Experience in building and deploying AI/ML based software to a cloud environment.</li></ul>
PREFERRED QUALIFICATION<ul><li>
Proficiency in Python and python-based AI/ML framework and familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch).</li><li>
Experience working with language models like LLM's APIs and optimizing their usage for specific applications.</li><li>
Experience with the following programming languages: Python, C++, Java, R, SQL</li><li>
Strong written &amp; verbal communication skills and ability to communicate complex technical concepts to non-technical stakeholders</li><li>
Strong client-facing skillsets in a consulting environment</li><li>
Strong cross-functional skills with the ability to collaborate with a variety of internal and client-side teams</li><li>
Entrepreneurial mindset with a curiosity and passion for emergent tech and driving innovation</li><li>
MS or PhD in related field preferred (computer science, engineering, etc.)</li></ul></div>

AI/ML Computational Science Senior Manager

JD.COM

<div>
 
 JD.com (NASDAQ: JD and HKEX: 9618), also known as JINGDONG, has evolved from a pioneering e-commerce platform into a leading technology and service provider with supply chain at its core. Renowned for its supply chain innovation and excellence, the company has expanded into sectors including retail, technology, logistics, healthcare, and more, aiming to transform traditional business models with cutting-edge digital solutions. Know more about us: https://corporate.jd.com/
 
 
 
 
 
 We are looking for a Machine Learning Engineer who is passionate about turning machine learning and research into real, large-scale business impact to build a next-generation inventory prediction &amp; replenishment optimisation platform at Joybuy (https://www.joybuy.com/). Joybuy is JD.com&rsquo;s European full-category online retail brand designed to bring customers a faster, more convenient, and cost-effective shopping experience. Offering same-day and next-day delivery across the UK, Joybuy combines speed, reliability, and affordability to meet the needs of modern shoppers. As a Machine Learning Engineer focusing on demand forecasting, replenishment optimisation, and supply chain decision intelligence, you will design, develop, and deploy models that support billions of dollars ' worth of global inventory flow decisions. Your work directly influences forecast accuracy, stock availability, fulfilment cost, and customer experience.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Design, implement, and evaluate machine learning models for demand forecasting, replenishment, and fulfilment optimisation.
 </li>
 <li>
 Develop innovative algorithmic solutions to complex forecasting and supply chain problems in close collaboration with product, engineering, and operations teams.
 </li>
 <li>
 Contribute to the technical direction of the fulfilment intelligence platform and influence model architecture, system design, and best practices.
 </li>
 <li>
 Work onsite in London, UK.
 </li>
 </ul>
 
 
 
 
 
 Minimum Qualifications
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Engineering, Computer Science, Mathematics, or a related technical field.
 </li>
 <li>
 3+ years of professional experience as a machine learning engineer or applied research scientist.
 </li>
 <li>
 Experience programming in Java, C++, Python or a related language
 </li>
 <li>
 Experience with neural deep learning methods and machine learning
 </li>
 <li>
 Demonstrated ability to work independently with minimal guidance; proactively manage tasks and priorities across multiple projects; analyze and execute work efficiently; collaborate effectively with cross-functional teams; and thrive in fast-paced, results-driven environments.
 </li>
 <li>
 Effective communication skills in English.
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications
 
 
 <ul>
 <li>
 Professional experience in e-commerce, retail forecasting, fulfilment logistics, inventory planning, or similar domains.
 </li>
 <li>
 Hands-on experience with modelling tools such as Python, PyTorch, TensorFlow, scikit-learn etc.
 </li>
 <li>
 Hands-on experience building large-scale distributed machine learning systems such as Hadoop, Spark etc.
 </li>
 <li>
 Effective communication skills in Chinese.
 </li>
 </ul>
 
 
 
 
 JD.com is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
 
 
 
 
 
 JR101321
 
</div>

Machine Learning Engineer - Fulfilment

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