mlops engineer

Fractal Analytics

<div>It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.<div><div>
Job Description</div></div><div><div><div><div><div><div><div><div><div><h1 class="jobSectionHeader">
EL3 &ndash; Databricks MLOps Engineer (Contract)</h1>
Domain: Claims Payment Integrity | M&amp;R, C&amp;S, E&amp;I Claims (preferred) 
Actuarial &amp; Forecasting Analytics Exposure is an Added Advantage 
Tech Stack: Databricks, Spark, Python, Scala, Azure, GitHub Actions, Terraform 
AI/LLM Capabilities: Embedding Models, LLM Integration, LangChain Agentic Frameworks<h2 class="jobSectionHeader">
Role Summary</h2>
The EL3 Databricks MLOps Engineer is a senior hands-on role responsible for enabling end-to-end machine learning lifecycle automation on Databricks. This includes building and maintaining the CI/CD infrastructure, environment configuration, packaging and deploying ML models, supporting reproducible experiments, and ensuring scalable job orchestration for AI/ML workloads, including LLM-based applications.
The role partners closely with Data Scientists, AI/ML Engineers, platform teams, and business stakeholders within Claims Payment Integrity to ensure robust, reliable, and automated ML delivery.<h2 class="jobSectionHeader">
Key Responsibilities</h2><ul><li>
Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks).</li><li>
Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable.</li><li>
Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools.</li><li>
Package, version, and deploy ML models into Databricks-managed execution environments.</li><li>
Set up automated workflows for training, retraining, evaluation, and scheduled job execution.</li><li>
Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models.</li><li>
Enable LLM/GenAI-driven solutions by integrating:<ul><li>
Embedding model generation</li><li>
RAG architectures</li><li>
Vector databases</li><li>
LangChain agentic workflows</li></ul></li><li>
Optimize resource usage, runtime configurations, and code execution patterns for ML workloads.</li><li>
Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines.</li><li>
Implement platform-level controls for environment consistency, dependency management, access control, and model versioning.</li><li>
Support troubleshooting, debugging, and performance improvements for ML workloads.</li><li>
Document standards, templates, guidelines, and best practices for MLOps teams.</li><li>
Work cross-functionally with product, engineering, and analytics teams across PI.</li></ul><h2 class="jobSectionHeader">
Required Qualifications</h2><ul><li>
Bachelor&rsquo;s/Master&rsquo;s degree in Computer Science, Engineering, or related field</li><li>
6&ndash;9 years of relevant experience in ML Engineering, MLOps, or platform engineering</li><li>
Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala</li><li>
Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration)</li><li>
Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps</li><li>
Experience deploying AI/ML models into cloud environments (Azure preferred)</li><li>
Ability to create and integrate embedding models, semantic vectors, and LLM-driven components</li><li>
Experience with LangChain for agentic workflows and integration of tools/functions</li><li>
Strong problem-solving, debugging, and collaboration skills</li></ul><h2 class="jobSectionHeader">
Preferred Qualifications</h2><ul><li>
Experience with Azure OpenAI or OpenAI-compatible LLM APIs</li><li>
Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing</li><li>
Experience in Agile/Scrum environments</li><li>
Strong understanding of software engineering best practices, packaging, dependency management</li></ul><h2 class="jobSectionHeader">
Good-to-Have Data Knowledge</h2><ul><li>
Call Center datasets (member &amp; provider interactions)</li><li>
Provider RCM datasets (billing, coding, authorizations)</li><li>
EHR/clinical datasets for cross-domain validation</li></ul></div></div></div></div></div></div></div></div></div>
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!<h3 class="jobSectionHeader">
Hiring Related Queries</h3>
India: HiringsupportIndia@fractal.ai
Outside India: HiringsupportROW@fractal.ai
This inbox does not process resume submissions. All applications must be made through posted job openings
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MLOps Engineer

XPS Group

<div>
 
 Location:
 
 London
 
 
 
 Employment Type:
 
 Permanent, Full Time
 
 
 
 Grade:
 
 Senior Associate/ Consultant
 
 
 Hybrid
 
 
 REQ003340
 
 
 
 About XPS Group
 
 
 
 XPS Group is a prominent and growing UK consultancy and administration firm within the pensions and insurance sectors. As a FTSE 250 company with over 2000 employees, we leverage expertise alongside advanced technology to serve over 1,400 pension schemes and their sponsors. Our goal is to foster a workplace where diverse talents thrive.
 
 
 
 About The Role
 
 
 
 Our Data Analytics business continues to grow, and we are now looking for an experienced and technical MLOps Engineer to join our vibrant London office with hybrid working. This is an exciting role and would most likely suit someone with previous experience in a similar role where they have gained knowledge and experience of designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments. You must have good technical knowledge of Phyton, SQL, CI/CD and familiar with Power BI.
 
 
 XPS Analytics is a specialist and multi-disciplinary team consisting of actuaries, data scientist and developers. Our role in this mission is to pioneer advancements in the field of pensions and beyond, leveraging state-of-the-art technology to extract valuable and timely insights from data. This enables the consultant to better advise Trustees and Corporate clients on a wide range of actuarial-related areas.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Model development: Work collaboratively with actuarial analysts to develop machine learning and statistical models to predict outcomes, related to pension schemes, such as life expectancy, default risk, or investment returns. Identify appropriate machine learning algorithms and apply them to enhance predictions, automate decision-making processes, and improve client offerings.
 </li>
 <li>
 Machine Learning Operations: Responsible for designing, deploying, maintaining and refining statistical and machine learning models using Azure ML. Optimize model performance and computational efficiency. Ensure that applications run smoothly and handle large-scare data efficiently. Implement and maintain monitoring of model drifts, data-quality alerts, scheduled r-training pipelines.
 </li>
 <li>
 Data Management and Preprocessing: Collect, clean and preprocess large datasets to facilitate analysis and model training. Implement data pipelines and ETL processes to ensure data availability and quality.
 </li>
 <li>
 Software Development: Write clean, efficient and scalable code in Python. Utilize CI/CD practices for version control, testing and code review.
 </li>
 <li>
 Work closely with actuarial analysts, actuarial modelling team (AMT) and other colleagues in XPS to integrate data science findings into practical advice and strategies.
 </li>
 <li>
 Stay abreast of new trends and technologies in Data Science technologies and pensions to identify opportunities for innovation.
 </li>
 <li>
 Provide training and support to other team members on using machine learning tools and understanding analytical techniques.
 </li>
 <li>
 Interpret and explain machine learning concepts and findings to other members of the analytics team and non-technical stakeholders within XPS.
 
 
 </li>
 </ul>
 
 Essential
 
 
 
 
 Your Profile:
 
 
 
 <ul>
 <li>
 Previous experience in designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments.
 </li>
 <li>
 Experience in data wrangling using Python, SQL and ADF.
 </li>
 <li>
 Experience in CI/CD and DevOps/MLOps and version control.
 </li>
 <li>
 Familiarity with data visualization and reporting tools, ideally PowerBI.
 </li>
 <li>
 Good written and verbal communication and interpersonal skills. Ability to convey technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience in the pensions or similar regulated financial services industry is highly desirable.
 </li>
 <li>
 Experience in working within a multidisciplinary team would be beneficial
 
 
 </li>
 </ul>
 
 What We Offer
 
 
 
 Enjoy a competitive salary, annual discretionary bonus, and 25 days&rsquo; holiday with buy/sell flexibility. Benefits include pension matching, healthcare plans, life assurance, and retailer discounts. We support our team with a flexible benefits scheme, employee assistance, and digital GP service. Participating in volunteering events is encouraged with paid volunteer days available. Referral bonuses are offered for introducing suitable candidates to XPS.
 
 
 
 How To Apply
 
 
 
 Interested candidates should submit applications via Apply Now option or contact recruitment@xpsplc.com for more information. Join XPS Group and contribute to a dynamic and inclusive workplace.
 
 
 
 Equal Opportunities Statement
 
 
 
 XPS Group is committed to diversity and equal opportunities. We welcome applications from all candidates, irrespective of sex, race, disability, sexual orientation, religion, or belief. As a Disability Confident employer, we ensure accessible and supportive work settings for all employees.
 
 
 
 Eligibility
 
 
 
 Any employment offer made will be conditional upon you satisfying DBS Disclosure checks, Employment or educational references, Satisfactory credit checks and eligibility to work in the UK before an offer can be made. XPS Group is not able to provide sponsorship to employees.
 
 
 
 Who Are We
 
 
 
 To find out more please visit: www.xpsgroup.com
</div>

Cleo

<div>
 
 About The Company
 
 
 
 
 
 About Cleo
 
 
 
 
 At Cleo, we're not just building another fintech app. We're embarking on a mission to fundamentally change humanity's relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. That's the future we're creating.
 
 
 Cleo is a rare success story: a profitable, fast-growing unicorn with over $300 million in ARR and growing over 2x year-over-year. This isn't just a job; it's a chance to join a team of brilliant, driven individuals who are passionate about making a real difference. We have an exceptionally high bar for talent, seeking individuals who are not only at the top of their field but also embody our culture of collaboration and positive impact.
 
 
 If you&rsquo;re driven by complex challenges that push your expertise, the chance to shape something truly transformative, and the potential to share in Cleo&rsquo;s success as we scale, while growing alongside a company that&rsquo;s scaling fast, this might be your perfect fit.
 
 
 Follow us on LinkedIn to keep up to date with new product features and insights from the team.
 
 
 What you&rsquo;ll be doing
 
 
 The right candidate will support our product teams in achieving their
 
 OKRs
 
 while championing best practices in
 
 data engineering and MLOps
 
 . In this role, you'll work closely with product teams to ensure they effectively adopt the tools, frameworks, and processes provided by the
 
 Data Platform team
 
 , enabling them to build scalable, efficient, and reliable data and ML solutions. You'll help teams implement
 
 robust data pipelines, model deployment workflows, monitoring strategies, and cost-efficient practices
 
 to improve their data-driven capabilities.
 
 
 At the same time, you'll act as a crucial
 
 bridge between product teams and the Data Platform team
 
 , gathering insights on real-world challenges, gaps, and pain points in the existing platform. By surfacing these issues and collaborating with the platform team, you'll contribute to the continuous improvement of our internal tooling and infrastructure, ensuring it better serves the needs of our engineers and data scientists. This is an opportunity to
 
 blend hands-on engineering with strategic impact
 
 , influencing both product success and the evolution of our data platform.
 
 
 
 About You
 
 
 
 You are passionate about making a positive difference in society by improving the financial health of our users. You align with our company values and engineering principles, which drive our ways of working and software delivery.
 
 
 
 Our Ideal Candidate Will Have Experience In
 
 
 
 <ul>
 <li>
 Data System design and breaking down work
 </li>
 <li>
 Solid experience with data eng language (python ideal)
 </li>
 <li>
 Knowledge of at least one distributed processing framework. Plus if its streaming (eg: PySpark, Flink)
 </li>
 <li>
 Containerisation &amp; orchestration; Docker, Kubernetes
 </li>
 <li>
 Infrastructure as Code; Terraform
 </li>
 <li>
 Software engineering and best practises - proficiency in Python (preferred), code quality and maintainability.
 </li>
 <li>
 Good knowledge of different storage types and when to use. OLTP, OLAP, S3
 </li>
 <li>
 Understanding value and product thinking
 </li>
 <li>
 Experience working cross-functionally; Ability to work with data scientists, software engineers, and product managers to align ML initiatives with business goals.
 
 
 </li>
 </ul>
 
 Nice To Haves
 
 
 
 <ul>
 <li>
 Experience running streaming platform and knowledge of stream &gt; table and table &gt; stream
 </li>
 <li>
 Deep technical knowledge of core data structures, distributed processing. Practical application &gt;&gt; theoretical knowledge. It important to have aptitude to understand concepts, but application and reasoning over value is more important
 </li>
 <li>
 Monitoring and alerting how it pertains to data system -&gt; also this is important, not strictly necessary as something that can be learnt
 </li>
 <li>
 Deploying APIs and systems outside of core data platform -&gt; moving more into the ability to deploy ML systems. Happy if this person has not done this, but willing to learn
 </li>
 <li>
 Experience working with Feature Stores
 </li>
 <li>
 Experience building and managing ML pipelines e.g: Kubeflow, MLflow, Airflow, Flyte.
 
 
 </li>
 </ul>
 Our Tech Stack
 
 
 Cleo is built as a Ruby on Rails monolith with a single React Native app frontend, utilising TypeScript. We also leverage Python for machine learning services and PostgreSQL for our database, all hosted on AWS. Our CI/CD pipeline is fully automated, with production deployments happening on every merge via Heroku. Our backend engineers deploy multiple times a week, and we release our frontend app to Google and Apple for review at least once a week.
 
 
 While we take a pragmatic approach, we place a strong emphasis on quality. Our code is peer-reviewed, and we maintain automated testing using Minitest and CircleCI. We're also actively working towards a more modular architecture, focusing on separating concerns to achieve all the benefits of microservices within a monolith, while progressively refactoring our code as we build new features. Everyone in the engineering team contributes to driving our technical strategy, voices &amp; ideas from all levels are valued: we are all owners at Cleo.
 
 
 What do you get for all your hard work?
 
 
 <ul>
 <li>
 A competitive compensation package (base + equity) with bi-annual reviews, aligned to our quarterly OKR planning cycles. You can view our public progression framework and salary bandings here: https://cleo-ai.progressionapp.com/
 </li>
 <li>
 Work at one of the fastest-growing tech startups, backed by top VC firms, Balderton &amp; EQT Ventures
 </li>
 <li>
 A clear progression plan. We want you to keep growing. That means trying new things, leading others, challenging the status quo and owning your impact. Always with our complete support.
 </li>
 <li>
 Flexibility. We can&rsquo;t fight for the world&rsquo;s financial health if we&rsquo;re not healthy ourselves. We work with everyone to make sure they have the balance they need to do their best work
 </li>
 <li>
 Work where you work best. We&rsquo;re a globally distributed team. Our Poland team works fully remotely, but we host virtual socials and an annual company offsite somewhere in Europe with all expenses paid.
 </li>
 <li>
 Other benefits;
 </li>
 <li>
 Company-wide performance reviews every 6 months
 </li>
 <li>
 Generous pay increases for high-performing team members
 </li>
 <li>
 Equity top-ups for team members getting promoted
 </li>
 <li>
 25 days annual leave a year + public holidays (+ an additional day for every year you spend at Cleo, up to 30 days)
 </li>
 <li>
 Private medical insurance with Alan
 </li>
 <li>
 1 month paid sabbatical after 4 years at Cleo
 </li>
 <li>
 Regular socials and activities, online and in-person
 </li>
 <li>
 We'll pay for your OpenAI subscription
 </li>
 <li>
 Online mental health support via Spill
 </li>
 <li>
 We&rsquo;ll be employing you through our EOR provider, Deel, and can discuss our current benefit offering directly.
 
 
 </li>
 </ul>
 
 We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.
 
 
 
 
 If there&rsquo;s anything we can do to accommodate your specific situation, please let us know.
 
 
 
 Compensation range
 
 
 <ul>
 <li>
 Poland: PLN1 - PLN1 gross annually*
 </li>
 <li>
 Other locations: Compensation will be discussed during the interview process
 </li>
 <li>
 Final compensation will be determined based on the candidate's qualifications, skills, and previous experience
 
 
 </li>
 </ul>
 
 
 By submitting this application, I confirm that all the information given by me in this application for employment and any additional documents attached hereto are true to the best of my knowledge and that I have not wilfully suppressed any material fact. I confirm I have disclosed if applicable any previous employment with Cleo AI. I accept that if any of the information given by me in this application is in any way false or incorrect, my application may be rejected, any offer of employment may be withdrawn or my employment with Cleo AI may be terminated summarily or I may be dismissed. By submitting this application, I agree that my personal data will be processed in accordance with Cleo AI's
 
 
 
 
 Candidate Privacy Notice
 
 
</div>

Harnham

<div>
 
 
 MLOps Engineer
 
 
 
 
 Amsterdam, Netherlands (hybrid working)
 
 
 
 
 Up to 107k annually + benefits
 
 
 
 
 
 
 This is an opportunity to work on large scale, real time machine learning systems that directly impact millions of digital transactions. You will join a growing ML platform team focused on delivering robust, production ready solutions across payments and fraud, with strong ownership and visibility from day one.
 
 
 
 
 
 
 The Company
 
 
 
 They are a global technology organisation operating at the intersection of data, payments, and digital experiences. Their platform supports high volume, real time transactions for well known international businesses, focusing on performance, reliability, and innovation. With a strong engineering culture, they invest heavily in data and machine learning to drive continuous improvement. Teams are collaborative, fast paced, and committed to delivering meaningful impact.
 
 
 
 
 
 
 The Role
 
 
 
 You will focus on building and scaling MLOps capabilities within a production environment, supporting a range of machine learning use cases.
 
 
 
 
 <ul>
 <li>
 Design and build scalable systems for training, deploying, and monitoring machine learning models
 </li>
 <li>
 Support real time and batch ML workflows across critical platform services
 </li>
 <li>
 Develop and scale feature store capabilities for both online and offline use cases
 </li>
 <li>
 Improve observability and monitoring across ML systems
 </li>
 <li>
 Collaborate closely with data scientists and engineers to productionise models
 </li>
 <li>
 Take ownership of end to end delivery of ML features and systems
 </li>
 <li>
 Contribute to best practices in MLOps, CI CD, and cloud infrastructure
 </li>
 </ul>
 
 
 
 
 
 Your Skills and Experience
 
 
 <ul>
 <li>
 Strong commercial experience in MLOps or machine learning engineering
 </li>
 <li>
 High proficiency in Python for production level systems
 </li>
 <li>
 Experience deploying and maintaining machine learning models in production environments
 </li>
 <li>
 Knowledge of monitoring, observability, and system reliability
 </li>
 <li>
 Exposure to cloud platforms, ideally AWS
 </li>
 <li>
 Familiarity with ML tools and frameworks such as TensorFlow, PyTorch, Spark, or similar
 </li>
 <li>
 Experience with tools such as Databricks, SageMaker, Kubeflow, or equivalent platforms is beneficial
 </li>
 <li>
 Strong communication skills with the ability to work across technical teams
 </li>
 <li>
 A curious mindset and a practical approach to solving complex problems
 </li>
 </ul>
 
 
 
 
 
 What They Offer
 
 
 <ul>
 <li>
 Competitive salary and benefits package
 </li>
 <li>
 Hybrid working model with a collaborative office environment in Amsterdam
 </li>
 <li>
 Opportunities to work on high impact, large scale ML systems
 </li>
 <li>
 Clear progression and career development within a growing team
 </li>
 <li>
 Supportive engineering culture with strong mentorship and ownership
 </li>
 </ul>
 
 
 
 
 
 How to Apply
 
 
 
 If you are interested in this Machine Learning Ops Engineer opportunity in Amsterdam, please apply with your CV for immediate consideration.
 
</div>

Senior MLOps Engineer

HSBC

<div>
 If you&rsquo;re looking for a career that will help you stand out, join HSBC, and fulfil your potential - whether you want a career that could take you to the top, or an exciting new direction, we offer opportunities, support and rewards that will take you further.
 
 
 We&rsquo;re one of the largest banking and financial services organisations in the world, with a network that covers more than 50 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people fulfil their hopes and realise their ambitions.
 
 
 We are seeking a MLOps Engineer (LLM/GenAI)
 
 
 In this fantastic role, you&rsquo;ll engineer production-grade infrastructure for modern AI: hosting LLMs and speech/embedding models, pushing inference performance on real hardware, and building repeatable fine-tuning pipelines that ship domain-adapted models into production.
 
 
 If you like hard performance problems, platform engineering, and seeing your work used broadly across a global organisation, this role is built for you
 
 
 As an HSBC employee in the UK, you&rsquo;ll have access to tailored professional development opportunities and a competitive pay and benefits package. This includes private healthcare for all UK-based employees, enhanced maternity and adoption pay and support when you return to work, and a contributory pension scheme with a generous employer contribution.
 
 
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Design, build, and operate scalable model hosting platforms for LLMs, embeddings, and STT/TTS across heterogeneous hardware
 </li>
 <li>
 Optimise inference for latency, throughput, and cost (e.g., quantisation, KV-cache optimisation, dynamic/continuous batching)
 </li>
 <li>
 Evaluate and integrate inference frameworks (e.g., vLLM, TensorRT-LLM, SGLang) to maximise performance on target hardware
 </li>
 <li>
 Own inference health/performance monitoring (latency, throughput, TTFT, memory, availability) and troubleshoot bottlenecks/deployment issues
 </li>
 <li>
 Build end-to-end fine-tuning pipelines (data prep &rarr; distributed training &rarr; validation) and integrate fine-tuned models into the hosting/inference stack
 
 
 </li>
 </ul>
 To be successful in this role you should have the following skills:
 
 
 <ul>
 <li>
 Extensive experience in building AI platforms covering model hosting/inference optimisation and fine-tuning pipelines (LLM experience strongly preferred)
 </li>
 <li>
 Strong Python and CUDA engineering; solid understanding of GPU/CPU architecture and HPC fundamentals
 </li>
 <li>
 Deep inference optimisation expertise: KV-cache, batching, quantisation (INT4/FP8/GPTQ/AWQ), operator optimisation, framework integration (vLLM/TensorRT-LLM/SGLang)
 </li>
 <li>
 Production hosting experience with Docker/Kubernetes and cloud platforms (AWS/GCP/Azure)
 </li>
 <li>
 End-to-end fine-tuning expertise: data preparation, distributed training, hyperparameter tuning, HF/Accelerate/LoRA/QLoRA, plus benchmarking/monitoring/troubleshooting
 
 
 </li>
 </ul>
 
 Opening up a world of opportunity.
 
 
 
 Being open to different points of view is important for our business and the communities we serve. At HSBC, we&rsquo;re dedicated to creating diverse and inclusive workplaces - no matter their gender, ethnicity, disability, religion, sexual orientation, socio-economic background or age. We are committed to removing barriers and ensuring careers at HSBC are inclusive and accessible for everyone to be at their best. We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long term conditions or neurodivergent candidates who meet the minimum criteria for the role.
 
 
 If you have a need that requires accommodations or changes during the recruitment process, please get in touch with our Recruitment Helpdesk via hsbc.recruitment@hsbc.com .
</div>

MLOps Engineer (LLM/GenAI)

hackajob

<div>
 
 
 hackajob
 
 is collaborating with
 
 HSBC
 
 to connect them with exceptional professionals for this role.
 
 
 
 If you&rsquo;re looking for a career that will help you stand out, join HSBC, and fulfil your potential - whether you want a career that could take you to the top, or an exciting new direction, we offer opportunities, support and rewards that will take you further.
 
 
 We&rsquo;re one of the largest banking and financial services organisations in the world, with a network that covers more than 50 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people fulfil their hopes and realise their ambitions.
 
 
 We are seeking a MLOps Engineer (LLM/GenAI)
 
 
 In this fantastic role, you&rsquo;ll engineer production-grade infrastructure for modern AI: hosting LLMs and speech/embedding models, pushing inference performance on real hardware, and building repeatable fine-tuning pipelines that ship domain-adapted models into production.
 
 
 If you like hard performance problems, platform engineering, and seeing your work used broadly across a global organisation, this role is built for you
 
 
 As an HSBC employee in the UK, you&rsquo;ll have access to tailored professional development opportunities and a competitive pay and benefits package. This includes private healthcare for all UK-based employees, enhanced maternity and adoption pay and support when you return to work, and a contributory pension scheme with a generous employer contribution.
 
 
 
 In This Role, You Will
 
 
 
 <ul>
 <li>
 Design, build, and operate scalable model hosting platforms for LLMs, embeddings, and STT/TTS across heterogeneous hardware
 </li>
 <li>
 Optimise inference for latency, throughput, and cost (e.g., quantisation, KV-cache optimisation, dynamic/continuous batching)
 </li>
 <li>
 Evaluate and integrate inference frameworks (e.g., vLLM, TensorRT-LLM, SGLang) to maximise performance on target hardware
 </li>
 <li>
 Own inference health/performance monitoring (latency, throughput, TTFT, memory, availability) and troubleshoot bottlenecks/deployment issues
 </li>
 <li>
 Build end-to-end fine-tuning pipelines (data prep &rarr; distributed training &rarr; validation) and integrate fine-tuned models into the hosting/inference stack
 
 
 </li>
 </ul>
 To be successful in this role you should have the following skills:
 
 
 <ul>
 <li>
 Extensive experience in building AI platforms covering model hosting/inference optimisation and fine-tuning pipelines (LLM experience strongly preferred)
 </li>
 <li>
 Strong Python and CUDA engineering; solid understanding of GPU/CPU architecture and HPC fundamentals
 </li>
 <li>
 Deep inference optimisation expertise: KV-cache, batching, quantisation (INT4/FP8/GPTQ/AWQ), operator optimisation, framework integration (vLLM/TensorRT-LLM/SGLang)
 </li>
 <li>
 Production hosting experience with Docker/Kubernetes and cloud platforms (AWS/GCP/Azure)
 </li>
 <li>
 End-to-end fine-tuning expertise: data preparation, distributed training, hyperparameter tuning, HF/Accelerate/LoRA/QLoRA, plus benchmarking/monitoring/troubleshooting
 
 
 </li>
 </ul>
 
 Opening up a world of opportunity.
 
 
 
 Being open to different points of view is important for our business and the communities we serve. At HSBC, we&rsquo;re dedicated to creating diverse and inclusive workplaces - no matter their gender, ethnicity, disability, religion, sexual orientation, socio-economic background or age. We are committed to removing barriers and ensuring careers at HSBC are inclusive and accessible for everyone to be at their best. We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long term conditions or neurodivergent candidates who meet the minimum criteria for the role.
 
 
 If you have a need that requires accommodations or changes during the recruitment process, please get in touch with our Recruitment Helpdesk via hsbc.recruitment@hsbc.com.
</div>

<div><ul><li>REQ-10117422</li>
<li>30/06/2026</li>
<li>IT Engineering</li>
<li>Warsaw, Poland</li>
<li>Katowice, Poland</li>
<li>Katowice</li>
<li>ING Hubs</li></ul> 
 
<div> Save for Later
</div><div><div><div><div><div><div>ING Hubs Poland is hiring!
The expected salary for this position: 9600 - 19000 PLN
The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.
<h2 class="jobSectionHeader">We are looking for you, if you:</h2>
<ul><li>have good understanding of machine learning model deployment and consumption patterns</li>
<li>have hands-on experience with workflow orchestration tools, especially Apache Airflow (must-have)</li>
<li>have experience with ML lifecycle management tools such as MLflow (strongly preferred)</li>
<li>have hands-on experience working with Google Cloud Platform (GCP) in the context of data or ML pipelines (e.g. BigQuery, Vertex AI, Cloud Storage or similar)</li>
<li>have experience in building containerized components (Docker)</li>
<li>have experience in CI/CD and DevOps practices</li>
<li>have hands-on experience with data pipelines and ETL processes</li>
<li>have hands-on experience with monitoring logging and troubleshooting ML pipelines</li>
<li>can clearly express ideas and collaborate effectively with data scientists and engineers</li>
<li>speak English at B2 level or above</li>
</ul><h2 class="jobSectionHeader">You'll get extra points for:</h2>
<ul><li>strong experience with Airflow-based workflow design and optimization</li>
<li>experience with Vertex AI in GCP</li>
<li>experience with Spark or distributed batch data processing</li>
<li>familiarity with Kedro or similar pipeline frameworks</li>
<li>experience with Kubernetes or distributed environments</li>
</ul><h2 class="jobSectionHeader">Your responsibilities:</h2>
<ul><li>developing and managing workflow orchestration using Airflow (core responsibility)</li>
<li>supporting and improving model lifecycle management using MLflow (tracking, registry, reproducibility)</li>
<li>actively contributing to the migration of the ML Batch platform from on‑premise (IPC) to Google Cloud Platform (GCP)</li>
<li>refactoring and adapting ML pipelines to run efficiently in cloud-native environments</li>
<li>developing and improving templates for productionizing ML solutions</li>
<li>integrating ML pipelines with CI/CD pipelines for automated deployments</li>
<li>ensuring scalability reliability and reproducibility of ML workloads</li>
<li>troubleshooting and optimizing pipelines to improve performance and stability</li>
<li>participating in on‑call support to maintain platform reliability</li>
<li>collaborating with stakeholders to deliver scalable secure and cost‑efficient solutions</li>
</ul><h2 class="jobSectionHeader">Information about the squad:</h2>
ML-Batch is a robust, scalable, and efficient platform provided by DAP. The goal of our team is to empower users by providing them with an easy-to-use platform for designing &amp; implementing batch processing, ETL, and machine learning pipelines. By leveraging cutting-edge tools like Airflow &amp; MLFlow and by adhering to MLOps methodology, we aim to facilitate seamless, high-performance data workflows, ensuring that our users can execute their data-driven tasks reliably and efficiently. MLOps practices ensure continuous integration, deployment, and monitoring, enabling a streamlined and collaborative approach to machine learning operations.
As an MLOps Engineer, you will:
<ul><li>help migrate ML pipelines and workflows to GCP</li>
<li>contribute to shaping the target ML platform architecture</li>
<li>work with technologies such as Airflow (orchestration), MLflow (model lifecycle), and Spark (data processing)</li>
</ul>You will join a multinational multi-cultural team delivering scalable secure and automated solutions that enable data scientists across ING to build high‑impact products.
You will work with modern technologies solve complex problems and help shape the future of ING&rsquo;s data ecosystem in a cloud‑first environment.
The role naming convention in the global ING job architecture will be &ldquo;Engineer III&rdquo;
</div>The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.
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<div><h3 class="jobSectionHeader">Questions? Just ask
 
ING Recruitment team</h3><div></div><div> Save for Later
</div></div></div></div></div></div><div><div><div>ING&rsquo;s vision is to unlock our people&rsquo;s full potential through our inclusive culture where everyone has the opportunity to develop and have impact for our customers and society. To achieve this vision, our policies support diversity, equity, and inclusion. As an equal opportunity employer, we do not tolerate discrimination of any kind with regard to age, gender, gender identity, cultural background, experience, religion, race, ethnicity, disability, family responsibilities, sexual orientation, social origin, or any other status protected by applicable law. If you require any assistance or if we can accommodate you in any way when participating in our application and/or interview process, please email the recruiting contact listed for the relevant position. We will be happy to work with you to ensure a fair and accessible process. Read more about our commitment to diversity, inclusion and belonging here.</div></div></div></div>

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