At a Glance
- Tasks: Drive MLOps strategy and implement scalable machine learning operations.
- Company: Join Quantiphi, an award-winning AI-first digital engineering company.
- Benefits: Remote work, competitive salary, and opportunities for continuous upskilling.
- Other info: Collaborate with a high-energy team and tackle meaningful challenges.
- Why this job: Make a real impact with Fortune 500 companies using cutting-edge AI technologies.
- Qualifications: 8+ years in ML/AI engineering with strong architectural skills.
The predicted salary is between 80000 - 100000 € per year.
About Quantiphi: Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients. Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries. Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation.
We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:
- 21 Google Cloud Partner of the Year awards in the past 10 years
- 3 AWS AI/ML Partner of the Year awards
- 3 NVIDIA Partner of the Year awards
- 3 Snowflake Partner of the Year awards
- Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms
Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture.
Role: MLOps Architect - AWS
Experience Level: 8+ years
Employment type: Full Time
Location: Remote (UK)
What you will do: We are seeking an experienced MLOps/AIOps Architect who can drive end-to-end implementation of MLOps strategy and also contribute broadly across other enterprise AI/ML programs. This role demands a strong architectural mindset, hands-on technical depth, and the ability to design scalable, cloud-native machine learning operations across traditional ML and modern LLM workflows.
The ideal candidate will bring experience with SageMaker-based MLOps pipelines, evaluation of equivalent tooling stacks, hybrid MLOps/LLMOps automation, CI/CD orchestration, governance, monitoring, automation and production-grade scalability patterns.
Key Responsibilities:
- Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
- Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation.
- Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
- Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.
- Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks.
- Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
- Define and implement standards for model deployment, monitoring, governance, and automation to ensure production-grade reliability and scalability.
- Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions.
- Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools.
- Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms.
- Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices.
Basic Qualifications (BQs):
- Experience working in ML/AI engineering or MLOps roles with strong architecture exposure.
- Strong experience in leading enterprise grade MLOps strategy and its execution.
- Proven experience in implementing the adoption of enterprise-grade MLOps platforms with client data science teams.
- Proven leadership in defining and executing enterprise MLOps strategy.
- Demonstrated success in driving the adoption of enterprise-grade MLOps platforms with client data science teams.
- Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent).
- Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
- Deep understanding of model lifecycle management (feature engineering->training → registry → deployment → monitoring).
- Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks.
- Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
- Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
- Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
- Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines.
- Experience with Kubernetes based development.
- Experience with feature engineering pipelines and Feature Store management.
- Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
- Hands-on experience with AWS Bedrock and Agentcore service.
- Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
- Strong foundation in Python and cloud-native development patterns.
- Solid understanding of security best practices, IAM, secrets management, and artifact governance.
Other Qualifications (OQs):
- Experience with vector databases, RAG pipelines, or multi-agent AI systems.
- Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK).
- Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments.
- Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
- SQL and data transformation experience using Snowflake, Databricks, Spark.
- Ability to translate business goals into scalable AI/ML platform designs.
- Strong communication and cross-team collaboration skills.
- Ability to guide engineering teams through technical uncertainty and design choices.
What is in it for you: Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale. Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges. Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents. Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
MLOps Architect - AWS in Peterborough employer: Quantiphi
Quantiphi is an exceptional employer that fosters a dynamic and inclusive work culture, empowering employees to drive innovation in AI and digital engineering. With a commitment to professional growth, team members have access to continuous learning opportunities and the chance to collaborate with industry leaders on transformative projects. Working remotely from the UK, you will be part of a globally recognised company that values its people and encourages meaningful contributions to impactful solutions.
StudySmarter Expert Advice🤫
We think this is how you could land MLOps Architect - AWS in Peterborough
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at meetups. A personal connection can often get you a foot in the door faster than any application.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your MLOps projects. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to MLOps. We recommend doing mock interviews with friends or using online platforms to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace MLOps Architect - AWS in Peterborough
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the MLOps Architect role. Highlight your experience with AWS, SageMaker, and any relevant MLOps tools. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about MLOps and how your background makes you a perfect fit for our team. Keep it engaging and personal—let us know who you are!
Showcase Your Projects:If you've worked on any cool projects related to MLOps or AI, make sure to mention them! We love seeing real-world applications of your skills, so don’t hold back on sharing your achievements.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our awesome team!
How to prepare for a job interview at Quantiphi
✨Know Your MLOps Inside Out
Make sure you have a solid grasp of MLOps principles, especially those related to AWS and SageMaker. Be prepared to discuss your experience with CI/CD pipelines, model lifecycle management, and any specific tools you've used like Kubeflow or MLflow.
✨Showcase Your Architectural Mindset
Quantiphi is looking for someone with a strong architectural mindset. Prepare to talk about how you've designed scalable, cloud-native ML operations in the past. Bring examples of your work that demonstrate your ability to solve complex challenges.
✨Collaboration is Key
This role involves working with cross-functional teams, so be ready to share experiences where you've collaborated with data engineers, DevOps, or client stakeholders. Highlight your communication skills and how you’ve navigated technical uncertainties in team settings.
✨Stay Current with Trends
The field of AI and MLOps is always evolving. Show your enthusiasm for continuous learning by discussing recent trends or technologies you've explored. Mention any relevant certifications or courses you've taken to stay ahead in the industry.
