ml ops engineer

BT Group

<div><div><div><div><div><h1 class="jobSectionHeader"><b>Senior ML Ops Engineer
</b></h1></div></div></div></div><div><div><div><div>Job Req ID: 56202
</div></div></div></div><div><div><div><div>Posting Date: 20 Feb 2026
</div></div></div></div><div><div><div><div>Function: Data &amp; AI
</div></div></div></div><div><div><div><div>Unit: Digital
</div></div></div></div><div><div><div><div>Location:
RMZ Ecoworld, Devarabeesanahal, Bengaluru, India
</div></div></div></div><div><div><div><div>Salary: Competitive
</div></div></div></div><div></div><div><div><div><div><p><b>Recruiter: Sowmya Reddy</b></p>
<p><b>
Hiring Manager: Julian Burns</b></p>
<p><b>
Career Grade: D</b></p>
<p><b>
Internal Closing Date: 26th February, 26</b></p><div><div><div><h2 class="jobSectionHeader"><b>
Why this job matters</b></h2>
</div><div><p>
At BT, we&rsquo;re transforming how data, machine learning, and AI drive our business and customer experiences. As a Senior ML Engineer, you&rsquo;ll play a pivotal role in industrialising ML and AI across BT, collaborating with diverse teams to deliver scalable, secure, and high-impact solutions.</p>
<p>
You&rsquo;ll architect and automate robust ML/AI pipelines&mdash;formulate real-time APIs and batch systems that scale, solving operational challenges like zero-downtime model updates, drift monitoring, incident response, and automated retraining, while ensuring systems are secure, cost-efficient, compliant, and smoothly transitioned into support.</p>
<p>
You&rsquo;ll accelerate ML productionisation by building infrastructure and tooling that enable data scientists to deploy models reliably, ensuring they work smoothly in production. As a senior figure in the ML Engineering team, you&rsquo;ll provide guidance, solve deployment challenges, and help business units realise value from AI initiatives faster.</p>
<p>
BT offers myriad opportunities to learn, develop, and expand your network. Continuous learning is essential&mdash;you&rsquo;ll be encouraged to keep your skills sharp, explore new tools, and share knowledge with the team. Cross-training on parallel platforms is available for those with strong experience translating ML and LLM experiments into enterprise-scale production systems.</p></div></div><div><div><h2 class="jobSectionHeader"><b>
What you&rsquo;ll be doing</b></h2>
</div><div><ul><li>Lead, mentor, and develop you engineering team, fostering a culture of learning and collaboration.</li>
<li>Architect and build on BT&rsquo;s MLOps stacks for fast, safe, and scalable ML/GenAI delivery with clear FinOps guardrails.</li>
<li>Design and implement production-grade ML/AI infrastructure, championing reusable patterns and best practices with Data Scientists, support, and engineering teams.</li>
<li>Embed FinOps, security, and data privacy into every stage of the ML/AI lifecycle.</li>
<li>Work closely with data scientists, engineers, and stakeholders to accelerate research-to-production using robust engineering practices and AI coding tools.</li>
<li>Define support strategies for long-term model health, including SLOs, drift monitoring, and feedback loops.</li>
<li>Lead deployment of LLM and GenAI services on platforms like Amazon Bedrock and Google Vertex AI.</li>
<li>Design and translate infrastructure for GenAI applications: vector databases, embeddings, retrieval/RAG, model gateways, GPU management, observability, and cost monitoring.</li>
<li>Promote experiment tracking and model management tools (e.g., Weights &amp; Biases).</li>
<li>Ensure strong software engineering practices: code review, testing, documentation, and version control.</li>
</ul></div></div><div><div><h2 class="jobSectionHeader"><b>What we are looking for from you</b></h2>
</div><div><ul><li>Bachelor&rsquo;s degree, MSc, or equivalent in Computer Science, Engineering, Mathematics, or related field.</li>
<li>Professional certifications in AWS and/or GCP (Architect, Engineering, or ML) are highly desirable.</li>
<li>5+ years in ML/AI engineering, including a minimum of 3+ years of hands-on experience in MLOps.</li>
<li>Deep expertise in at least one major cloud platform (AWS, GCP); knowledge of Vertex AI or equivalent required.</li>
<li>Proven experience building, debugging, and deploying ML pipelines for large-scale, high-throughput, low-latency applications.</li>
<li>Production-level fluency managing components in Python, Docker, and deploying ML/AI services (e.g., FastAPI). Supporting skills in SQL and advanced use of</li>
<li>Terraform, Pulumi, or AWS CDK.</li>
<li>Advanced expertise in CI/CD pipelines (GitLab CI, GitHub Actions) and MLOps pipelining services (Kubeflow, TFX, Kedro, or MLflow).</li>
<li>Practical experience deploying LLMs and other AI models, with understanding of sourcing, performance, quantization, batching, inference service management, metrics, and design trade-offs.</li>
<li>Demonstrated experience managing FinOps, security, and data privacy in ML/AI systems.</li>
<li>Proven ability to work directly with data scientists, stakeholders, and as part of Agile squads.</li>
<li>Experience leading, mentoring, and developing a positive engineering team culture.</li>
<li>Personal commitment to continuous learning and professional development.</li>
</ul></div></div><div><div><h2 class="jobSectionHeader"><b>Our leadership standards</b></h2>
</div><div><p><b>
Looking in:</b><br>
<b>Leading inclusively and Safely</b><br>
I inspire and build trust through self-awareness, honesty and integrity.<br>
<b>Owning outcomes</b><br>
I take the right decisions that benefit the broader organisation.</p>
<p><b>Looking out:</b><br>
<b>Delivering for the customer</b><br>
I execute brilliantly on clear priorities that add value to our customers and the wider business.<br>
<b>Commercially savvy</b><br>
I demonstrate strong commercial focus, bringing an external perspective to decision-making.</p>
<p><b>Looking to the future:</b><br>
<b>Growth mindset</b><br>
I experiment and identify opportunities for growth for both myself and the organisation.<br>
<b>Building for the future</b><br>
I build diverse future-ready teams where all individuals can be at their best.</p></div></div></div><p><b>
About us</b></p><br>
<p></p>
<p>
BT Group was the world&rsquo;s first telco and our heritage in the sector is unrivalled. As home to several of the UK&rsquo;s most recognised and cherished brands &ndash; BT, EE, Openreach and Plusnet, we have always played a critical role in creating the future, and we have reached an inflection point in the transformation of our business.<br>
<br>
Over the next two years, we will complete the UK&rsquo;s largest and most successful digital infrastructure project &ndash; connecting more than 25 million premises to full fibre broadband. Together with our heavy investment in 5G, we play a central role in revolutionising how people connect with each other.<br>
<br>
While we are through the most capital-intensive phase of our fibre investment, meaning we can reward our shareholders for their commitment and patience, we are absolutely focused on how we organise ourselves in the best way to serve our customers in the years to come. This includes radical simplification of systems, structures, and processes on a huge scale. Together with our application of AI and technology, we are on a path to creating the UK&rsquo;s best telco, reimagining the customer experience and relationship with one of this country&rsquo;s biggest infrastructure companies.<br>
<br>
Change on the scale we will all experience in the coming years is unprecedented. BT Group is committed to being the driving force behind improving connectivity for millions and there has never been a more exciting time to join a company and leadership team with the skills, experience, creativity, and passion to take this company into a new era.</p><br>
<p></p>
<p><b>
A FEW POINTS TO NOTE:</b></p><br>
<p></p>
<p>
Although these roles are listed as full-time, if you&rsquo;re a job share partnership, work reduced hours, or any other way of working flexibly, please still get in touch.</p>
<p>
We will also offer reasonable adjustments for the selection process if required, so please do not hesitate to inform us.</p><br>
<p></p>
<p><b>
DON'T MEET EVERY SINGLE REQUIREMENT?</b></p><br>
<p></p>
<p>
Studies have shown that women and people who are disabled, LGBTQ+, neurodiverse or from ethnic minority backgrounds are less likely to apply for jobs unless they meet every single qualification and criteria. We're committed to building a diverse, inclusive, and authentic workplace where everyone can be their best, so if you're excited about this role but your past experience doesn't align perfectly with every requirement on the Job Description, please apply anyway - you may just be the right candidate for this or other roles in our wider team.</p></div></div></div></div></div><p></p>

Senior ML Ops Engineer

Fractal Analytics

<div><div>It's fun to work in a company where people truly BELIEVE in what they are doing!</div><div></div><div><i>
We're committed to bringing passion and customer focus to the business.</i></div><div></div><div>
Role: MLOps Platform Engineer</div><div>
Experience: +5 years</div><div>
Overview: We are seeking a skilled MLOps Platform Engineer to build and maintain a robust platform for the entire Machine Learning (ML) lifecycle. This role involves automating ML model training, testing, deployment, and monitoring, integrating seamlessly with our existing infrastructure to accelerate ML innovation.</div><div>
Key responsibilities:</div><ul><li><div>
CI/CD for ML: Develop and manage CI/CD pipelines for ML model code and infrastructure, covering unit, integration, and deployment to all environments.</div></li><li><div>
Automated ML Training: Design and implement pipelines for repeatable model training, automatic sweeps, data processing, and hyperparameter optimization.</div><ul><li><div>
Include scheduling, queuing, and cost monitoring for training runs.</div></li><li><div>
Support training on cloud and on-premise GPU resources.</div></li></ul></li><li><div>
Model Monitoring: Establish tools and dashboards for continuous monitoring of deployed ML models, ensuring health, availability, and performance.</div></li><li><div>
Platform Integration: Ensure the MLOps platform integrates with company workflows, data pipelines, and computing architecture.</div></li><li><div>
MLOps Best Practices: Promote and implement best practices for reproducibility, version control, and governance across the ML lifecycle.</div></li></ul><div>
Skills:</div><ul><li><div>
Strong experience MLOps, DevOps, or ML Engineering, focusing on ML infrastructure</div></li><li><div>
Programming: Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).</div></li><li><div>
CI/CD &amp; Orchestration: Experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and workflow orchestrators (e.g., Airflow).</div></li><li><div>
Model Serving &amp; De</div></li></ul><div></div><div>
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!</div><div></div><div><div>
Not the right fit? Let us know you're interested in a future opportunity by clicking <i>Introduce Yourself</i> in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!</div></div></div>

ML Ops Engineer

Luxoft

<div><div><h5 class="jobSectionHeader"><b>Project description
</b></h5><p>We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.<br>
The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil &amp; gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.
</p></div><div><h5 class="jobSectionHeader"><b>Responsibilities</b></h5>
<div><div><p>Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
</p></div><div><p>Implement model governance strategies&mdash;versioning, rollback, audit trails, and explainability.
</p></div><div><p>Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
</p></div><div><p>Collaborate with software, IT, and data teams to ensure model integration and compliance.
</p></div><div><p>Support continuous improvement of ML lifecycle management practices and tooling.
</p></div><div><p>Business trip to Kuwait for first 6-12 months. On-site
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Skills</b></h5>
<p>Must have
</p><div><div><p>Strong software engineering background with ML Ops or DevOps exposure
</p></div><div><p>Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
</p></div><div><p>Proficiency in CI/CD workflows and scripting for automation.
</p></div><div><p>Familiarity with model monitoring, drift detection, and alerting frameworks.
</p></div><div><p>Experience deploying Python-based ML models in production.
</p></div><div><p>Ready for a long term business trip to Kuwait for first 6-12 months
</p></div></div><p>Nice to have
</p><div><div><p>Understanding of Digital Oilfield environments or OT/IT integration.
</p></div><div><p>Familiarity with cloud-native services&mdash;Azure is preferred.
</p></div><div><p>Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
</p></div><div><p>Understanding of security, compliance, and data access protocols in enterprise environments.
</p></div><div><p>Certifications:
</p></div><div><p>Azure DevOps or Azure AI Engineer certification is a plus.
</p></div><div><p>AWS DevOps Engineer certification is nice to have but not required
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Other</b></h5>
<div><div><div><p>Languages</p>
<p>English: C1 Advanced
</p></div></div><div><div><p>Seniority</p>
<p>Senior</p></div></div></div></div></div>
<br><div><div><div><p>Remote Egypt, Egypt
</p></div><p>Req. VR-118737
</p></div><div></div><div><div><p>DevOps
</p></div><div><p>Cross Industry Solutions</p>
</div><div><p>03/03/2026
</p></div><div><p>Req. VR-118737</p></div></div></div><p></p>

ML Ops Engineer (onsite work in Kuwait)

CLOFTECH (PRIVATE) LIMITED

<p><b>AI/ML Engineer</b></p><p>Experience: 2-3+ Years</p><p>Location: Lahore, Pakistan (Hybrid/Remote)</p><p>Type: Full-Time/Part Time</p><p><b>About the Role</b></p><p>We're seeking an AI/ML Engineer to develop and deploy our Named Entity Recognition (NER) service and predictive analytics capabilities. You'll build ML models that automatically infer semantic meaning from data columns, enabling intelligent schema mapping and automated KPI suggestions.</p><p><b>What You'll Do</b></p><ul><li>Develop and fine-tune NER models (BERT-based) for semantic column inference</li><li>Build and deploy ML models on Amazon SageMaker with real-time inference endpoints</li><li>Create training pipelines for continuous model improvement using customer data patterns</li><li>Implement confidence scoring and threshold logic for auto-mapping vs. human review</li><li>Develop predictive analytics models for customer KPIs (sales trends, utilization, forecasting)</li><li>Build LLM-powered chatbot capabilities for natural language data queries</li><li>Optimize model inference latency and implement batching strategies</li><li>Monitor model performance and implement A/B testing for model versions</li></ul><p><b>Requirements</b></p><ul><li>2-3+ years of experience in ML engineering or applied AI</li><li>Strong Python skills with ML frameworks (PyTorch, TensorFlow, or Hugging Face)</li><li>Experience with NLP tasks (NER, text classification, embeddings)</li><li>Hands-on experience deploying models to production (SageMaker, Docker, or similar)</li><li>Understanding of transformer architectures (BERT, GPT)</li><li>Experience with ML experiment tracking (MLflow, Weights &amp; Biases)</li><li>Familiarity with AWS ML services (SageMaker, Comprehend)</li><li>Strong understanding of ML ops practices and model monitoring</li></ul><p><b>Nice to Have</b></p><ul><li>Experience with LLMs and RAG (Retrieval Augmented Generation) architectures</li><li>Knowledge of vector databases (Pinecone, Weaviate, or pgvector)</li><li>Experience building conversational AI / chatbots</li><li>Background in data quality or schema matching problems</li></ul><p>Job Types: Full-time, Part-time</p><p>Pay: Rs250,000.00 - Rs350,000.00 per month</p><p>Expected hours: 20 &ndash; 40 per week</p><p>Experience:</p><ul><li>AI/ML: 2 years (Required)</li></ul><p>Work Location: In person</p>

AI/ML Engineer

Exelo Technologies Inc

<p>Must have significant AI/ML experience including deep learning approaches Ability to come up with ML solutions addressing complex business problems Productized ML solutions through ML/Ops Experience engineering production grade Gen AI and Agentic solutions</p><p>Job Types: Full-time, Permanent</p><p>Pay: ₹50,000.00 - ₹80,000.00 per month</p><p>Work Location: In person</p>

Data Scientist I

Qualcomm

<div>
 <strong>
  Company
  <br/>
  <br/>
 </strong>
 Qualcomm India Private Limited
 <br/>
 <br/>
 <strong>
  Job Area
  <br/>
  <br/>
 </strong>
 Engineering Group, Engineering Group &gt; Software Engineering
 <br/>
 <br/>
 <strong>
  General Summary
  <br/>
  <br/>
 </strong>
 <strong>
  Software Engineer &mdash; Machine Learning for EDA Systems
  <br/>
  <br/>
 </strong>
 Minimum Experience: 2+ years in software development
 <br/>
 <br/>
 🌟 Role Overview
 <br/>
 <br/>
 We are looking for a
 <strong>
  seasoned Software developer with 2-5 years experience
 </strong>
 in designing, developing, deploying, and scaling software platforms in the realm of
 <strong>
  Electronic Design Automation (EDA).
 </strong>
 You will work in cutting-edge initiatives that
 <strong>
  fuse Machine Learning with semiconductor design,
 </strong>
 driving robust systems that transform chip development workflows.
 <br/>
 <br/>
 <strong>
  Minimum Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 🏗️ Key Responsibilities
 <br/>
 <br/>
 <strong>
  Design and develop end-to-end ML-integrated software platforms
 </strong>
 built for scale, performance, and adaptability.
 <br/>
 <br/>
 <strong>
  Strong software development skills
 </strong>
 with experience in
 <strong>
  training and productizing Machine learning models
 </strong>
 , fostering innovation and engineering excellence.
 <br/>
 <br/>
 Good
 <strong>
  software design skills
 </strong>
 , knowledge of software development life cycle, embedding best development practices and agility across teams.
 <br/>
 <br/>
 Help in advanced software algorithm design tailored for automation and optimization in EDA flows.
 <br/>
 <br/>
 Engineer reliable and scalable data pipelines to fuel ML model development and inference.
 <br/>
 <br/>
 Knowledge of
 <strong>
  full-stack deployment strategies
 </strong>
 , enabling ML applications to function across diverse environments.
 <br/>
 <br/>
 Establish and maintain comprehensive
 <strong>
  ML Ops infrastructure
 </strong>
 , supporting continuous training, deployment, and monitoring.
 <br/>
 <br/>
 Experience with CI/CD pipelines, quality assurance standards, and system scalability from prototype to production.
 <br/>
 <br/>
 Ensure reliability and scalability of large system deployments for high-performance compute platforms.
 <br/>
 <br/>
 <strong>
  💡 Required Skills &amp; Experience
  <br/>
  <br/>
 </strong>
 At least 2+ years of experience in software development, with a focus on building and maintaining scalable machine learning projects.
 <br/>
 <br/>
 Strong coding chops in one or more programming languages -
 <strong>
  Python , C++, Typescript
 </strong>
 and frameworks
 <strong>
  like TensorFlow, PyTorch, or Scikit-learn
 </strong>
 with knowledge of software development tools and best practices.
 <br/>
 <br/>
 Solid grasp
 <strong>
  of CI/CD tools
 </strong>
 (e.g. GitLab, Jenkins) and working knowledge on
 <strong>
  MLOps
 </strong>
 platforms (e.g., MLflow, Kubeflow, Airflow)
 <br/>
 <br/>
 Handson in various databases and skilled in
 <strong>
  data engineering
 </strong>
 and cloud/on-prem infrastructures (AWS, GCP, Kubernetes).
 <br/>
 <br/>
 Sharp communication skills with a track record of cross-functional collaboration.
 <br/>
 <br/>
 <strong>
  🏆 Preferred Qualifications
  <br/>
  <br/>
 </strong>
 MS or PhD in Computer Science, Electrical Engineering, or a related field.
 <br/>
 <br/>
 <strong>
  Strong software coding and software design skills in Python / C++ . Should be aware of best software development practices, frameworks and tools
 </strong>
 .
 <br/>
 <br/>
 Knowledge in advanced ML techniques&mdash;e.g., graph neural networks or reinforcement learning, Generative AI is preferred.
 <br/>
 <br/>
 Contributions to open-source, patents, or technical publications related to ML-driven design automation.
 <br/>
 <br/>
 <strong>
  Knowledge of EDA tools and flows is preferrable
 </strong>
 , including experience with platforms such as Synopsys, Cadence, or Siemens.
 <br/>
 <br/>
 <strong>
  Applicants
 </strong>
 : Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
 <br/>
 <br/>
 Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
 <br/>
 <br/>
 <strong>
  To all Staffing and Recruiting Agencies
 </strong>
 : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
 <br/>
 <br/>
 If you would like more information about this role, please contact Qualcomm Careers.
</div>


Software Engineer - Machine Learning for EDA Systems

Onclusive

<div><p><b>About the Role:</b></p>
<p>
As a Machine Learning Operation Engineer, you will work on deploying, scaling, and optimizing backend algorithms, robust and scalable data ingestion pipelines, machine learning services, and data platforms to support analysis on vast amounts of text and analytics data. You will apply your technical knowledge and Big Data analytics on Onclusive&rsquo;s billions of online content data points to solve challenging marketing problems. ML Ops Engineers are integral to the success of Onclusive.</p>
<p><b>
Your responsibilities:</b></p>
<ul>
<li><p>Design and build scalable machine learning services and data platforms.</p>
</li>
<li><p>Utilize benchmarks, metrics, and monitoring to measure and improve services.</p>
</li>
<li><p>Manage system currently processing data on the order of tens of millions of jobs per day.</p>
</li>
<li><p>Research, design, implement and validate cutting-edge algorithms to analyze diverse sources of data to achieve targeted outcomes.</p>
</li>
<li><p>Work with data scientists and machine learning engineers to implement ML, AI, and NLP techniques for article analysis and attribution.</p>
</li>
<li><p>Deploy, manage, and optimize inference services on autoscaling fleets with GPUs and specialized inference hardware.</p><br>
</li>
</ul><p><b>Who you are:</b></p>
<ul>
<li><p>A degree (BS, MS, or Ph.D.) in Computer Science or a related field, accompanied by hands-on experience.</p>
</li>
<li>Minimum 3 years experience of Kubernetes and/or Terraform</li>
<li>
<p>Proficiency in Python, showcasing your understanding of Object-Oriented Programming (OOP) principles.</p>
</li>
<li><p>Solid knowledge of containerisation (Docker preferable)</p>
</li>
<li><p>Experience in Infrastructure as Code (IAC) for AWS, with a preference for Terraform.</p>
</li>
<li><p>Knowledge of Version Control Systems (VCS), particularly Git and GitHub, alongside familiarity with CI/CD, preferably GitHub Actions.</p>
</li>
<li><p>Understanding of release management, embracing rigorous testing, validation, and quality assurance protocols.</p>
</li>
<li><p>Good understanding of ML principles</p>
</li>
<li><p>Data Engineering experience (airflow, dbt, meltano) is highly desired.</p>
</li>
<li><p>Exposure to deep learning tech-stacks like Torch / Tensorflow, and Transformers.</p><br>
</li>
</ul><p><b>What we can offer:</b></p>
<p>
We are a global fast growing company which offers a variety of opportunities for you to develop your skill set and career. In exchange for your contribution, we can offer you:</p>
<ul>
<li><p>Competitive salary and benefits</p>
</li>
<li><p>Hybrid working in a team that is passionate about the work we deliver and supporting the development of those that we work with</p>
</li>
<li><p>A company focus on wellbeing and work life balance including initiative&rsquo;s such as flexible working and mental health support</p>
</li>
</ul><p><b>We want the best talent available, regardless of race, religion, gender, gender reassignment, sexual orientation, marital status, pregnancy, disability or age.</b></p></div>

Senior Machine Learning Operations Engineer

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