Are you passionate about turning complex business challenges into reliable, production-ready AI and machine learning solutions? We're seeking a hands-on MLOps Engineer to join our consulting team.
This is a cross-domain engineering role spanning solution architecture, cloud infrastructure, software development, data science, and machine learning operations. You will help clients move from experimentation to production by designing and building secure, scalable platforms and delivery practices across the full AI/ML lifecycle. You may work with traditional machine learning, generative AI, agentic systems, or other data-intensive solutions depending on the client context.
The ideal candidate combines a strong working grasp of data science and machine learning with engineering rigour: they can reason about system architecture, provision cloud infrastructure with Terraform, build automated delivery pipelines, write production-quality code and scripts, and operate reliable services in production. Experience with Google Cloud is preferred, but practical experience on AWS or Azure is also valuable.
What You'll Design, Build & Own
- Build end-to-end solutions: Translate business and data challenges into scalable, secure, observable technical architectures. Communicate trade-offs clearly and produce practical designs that can be implemented by client and delivery teams.
- Build cloud foundations and infrastructure as code: Design and provision environments using Terraform, apply sound networking, identity, security, secrets-management, and cost-management practices, and make infrastructure repeatable across development, test, and production.
- Engineer production systems: Write clean, tested, maintainable Python and other code, automation, and operational scripts. Build data and ML services, APIs, batch and event-driven workloads, and integrations that are reliable and fit for purpose.
- Automate delivery and operations: Build CI/CD and CI/CT pipelines for application, data, and ML workloads. Establish versioning, testing, packaging, deployment, approval, rollback, and environment-promotion practices using modern source-control and pipeline tooling.
- Own the ML and AI lifecycle: Develop repeatable workflows for data validation, experimentation, training, evaluation, deployment, monitoring, drift detection, incident response, and continuous improvement. Where relevant, build and evaluate LLM, RAG, and agentic AI capabilities using platforms such as Vertex AI.
- We are looking for a blend of technical depth, engineering discipline, consulting mindset, and creative problem-solving.
- Hands-on experience taking data science or machine learning work from exploration through deployment and ongoing operation in production.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Data Science, AI, or a related quantitative field; equivalent practical experience is also valued.
- Software engineering: Strong Python development skills, including writing tested, maintainable production code, APIs, automation, and command-line or operational scripts. Familiarity with Git-based development and code review practices.
- Data science and ML: Practical experience with libraries such as Scikit-learn, TensorFlow, and/or PyTorch; sound understanding of model development, evaluation, feature engineering, and algorithms such as classification, regression, clustering, or forecasting.
- Generative AI: Experience with LLM applications, prompt engineering, RAG, evaluation, and frameworks such as LangChain, LlamaIndex, or similar is desirable. Experience with agentic systems is a plus, not a prerequisite for every project.
- Data engineering and platforms: Strong SQL, Pandas, and NumPy skills, with an understanding of data quality, lineage, storage, batch and streaming patterns, and reproducible data pipelines.
- MLOps and platform engineering: Hands-on experience with Docker and Kubernetes or managed container platforms; model and service packaging; registries; orchestration; experiment/model/data versioning; feature or model stores; monitoring, logging, tracing, alerting, and production support.
- Infrastructure and cloud: Experience with Terraform or another infrastructure-as-code tool, cloud networking and IAM, and at least one major cloud platform (GCP preferred; AWS or Azure also valued).
Desirable Skills
- Experience designing and implementing CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Azure DevOps, Cloud Build, or similar, including automated testing and deployment gates.
- Experience with Terraform modules, Kubernetes, Helm, Argo CD, MLflow, Kubeflow, Vertex AI, or equivalent platform and MLOps tooling.
- Consulting experience: comfortable facilitating discovery, working across technical and non-technical stakeholders, explaining architecture and trade-offs, producing documentation, and delivering pragmatic solutions across different industries and technology stacks.
Discover some of the global benefits that empower our people to become the best version of themselves:
- Finance: Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus;
- Career Development: Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership;
- Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences;
- Work-Life Balance: Hybrid work and flexible working hours, employee assistance programme;
- Health: Global internal wellbeing programme, access to wellbeing apps;
- Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.
At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.