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"deployment engineer" Jobs in United Kingdom

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Senior Forward Deployment Engineer

Luxoft · London

SeniorFull-timeOnsite

Project description The project is focused on improving the security, release reliability, maintainability, and operational resilience of business-critical banking applications. Forward Deployed Engineers will conduct ta

Skills

The role demands extensive hands-on software engineering experience, with a focus on developing and maintaining production applications. Candidates must actively write and configure code, not merely oversee architecture or management tasks. Expertise in Java and JVM-based development is essential, while strong TypeScript skills are valued for Node.js-centric projects. Proficiency with the Spring ecosystem and modernizing Java enterprise applications is required. The candidate should demonstrate recent personal involvement in coding, debugging, and reviewing production code. Hard skills include practical experience in Test-Driven Development across multiple testing frameworks, with a solid understanding of various testing types such as unit, integration, and regression tests. The role involves creating automated test suites integrated within CI/CD pipelines, along with designing release automation and quality gates. Strong knowledge of secure coding practices, vulnerability remediation, and dependency risk management is necessary, including the ability to analyze application impacts before applying security fixes. Experience upgrading runtimes, frameworks, libraries, container platforms like Docker or OpenShift, and managing Linux environments—preferably Red Hat Enterprise Linux—is also important. Familiarity with build tools such as Maven, Gradle, or npm, advanced Git workflows, and comprehensive production-debugging techniques is expected. Candidates should understand application observability, exception handling, and production readiness, as well as fundamental distributed and critical system engineering principles. Soft skills focus on demonstrated ownership of complex technical work through all stages, from design to production handover. Strong client-facing communication is essential, including leading technical discussions, constructively challenging unsafe practices, and explaining trade-offs to diverse stakeholders. The ability to coach engineers and implement sustainable engineering standards without excessive customization is critical. Responsible and active use of AI coding tools to enhance engineering, testing, and delivery outcomes is also expected. Hard skills: Java/JVM and optionally TypeScript development with Spring ecosystem expertise Test-Driven Development and automated testing within CI/CD frameworks Secure coding, vulnerability management, and dependency risk analysis Runtime, framework, library upgrades, and container platform management Linux system administration, build tools, Git workflows, and production debugging Application observability and distributed system engineering principles Soft skills: End-to-end ownership of technical projects Effective client communication and technical leadership Coaching and sustainable standards implementation Proficient use of AI-assisted development tools

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Forward Deployment Engineer – AI Data Platforms & Data Spaces

NTT DATA · London

MidFull-timeOnsite

We are expanding our team: As a Forward Deployment Engineer, you will design and deploy AI-ready data platforms and sovereign data spaces to support clients’ AI transformation. Engage directly with customers to assess da

Discovered 2 days ago

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AI Agentic Engineer/ Forward Deployment Engineer

Cognizant · Manchester

SeniorFull-timeHybrid

Your next challenge Bring your AI engineering expertise to a role where you can turn complex commercial challenges into practical, scalable solutions. As an AI Agentic Engineer/ AI Forward Deployment Engineer, you’ll de

Skills

AWS BedrockAWSGCP or AzurePHP

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Deployment Engineer - London

Celonis · London

SeniorFull-timeHybrid

Celonis is the trusted platform to industrialize Enterprise AI. At our core is the Celonis Context Model — which combines process data, business knowledge, and intelligence into a living digital twin of the enterprise th

Skills

PQLSQLPythonClaude Code

Discovered 1 weeks ago

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Deployment Engineer - London

Celonis · London

MidOnsite

Celonis is the trusted platform to industrialize Enterprise AI. At our core is the Celonis Context Model — which combines process data, business knowledge, and intelligence into a living digital twin of the enterprise th

Skills

SQLPythonSAPOracle

Discovered 1 weeks ago

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Commissioning & Field Deployment Engineer

Hive Autonomy · London

MidFull-timeRemote

At Hive, we're building the defining physical AI platform of the next decade. Industrial labor is one of the most acute bottlenecks in the global economy. Operators are expensive, hard to retain, and working in environme

Skills

hivelinux

Discovered 2 weeks ago

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Senior Customer Deployment Engineer

Valarian · London

SeniorFull-timeHybrid

Valarian Technologies is a dual-use technology company building critical tools to safeguard the future in an era of evolving global security challenges. We're rethinking security beyond traditional military domains, addr

Skills

ansibleargokubernetesterraform

Discovered 3 weeks ago

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Product Deployment Engineer BS/MS, Intern - 2027 (English)

Google · London

InternOnsite

As a Product Deployment Engineer Intern in gTech Ads, you will gain hands-on exposure to how Google bridges customer business goals with advertising technologies. You will serve as the in-market technical and product aut

Skills

gcpjavascriptpythonsql

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Fiber Deployment Engineer

nscale ·

MidOnsite

Please note this role will require extensive travel across EMEA & the Nordics ~50%+ Nscale is the GPU cloud engineered for AI. We provide cost-effective, high-performance infrastructure for AI start-ups and large enterpr

Skills

jirapower-bitableau

Discovered 4 weeks ago

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Senior Network Engineer – Deployment

Nscale ·

SeniorFull-timeOnsite

. About Nscale Nscale is the GPU cloud engineered for AI. We provide cost-effective, high-performance infrastructure for AI start-ups and large enterprise customers. Nscale enables AI-focused companies to achieve super

Skills

SONiCCumulusDWDM

Discovered 4 weeks ago

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Principal Machine Learning Engineer

Rowden Technologies · Bristol

SeniorHybrid

We’re building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience. We specialise in turning advances in sensing, AI, and communications into o

Skills

Essential: - Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. - Proven delivery: you have made decisions that mattered and lived with the outcomes. You have led technical work into production, or matured it to a high technology readiness level (TRL) ready for deployment. - Deep ML and DL expertise: mastery across modern machine learning and deep learning, with strong grounding in the maths and the ability to reason about trade-offs in production. - Large model training: deep, hands-on experience training and fine-tuning large models, including distributed training across multi-GPU or multi-node environments and writing highly performant model training code. - LLMs & agentic systems: a good level of knowledge and practical experience with LLM and agent-based systems—retrieval/RAG, tool orchestration, prompt optimisation and evaluation. - Data and edge experience: practical experience with data engineering and deploying to edge or embedded hardware, enough to design systems that work across the whole pipeline. - Software development: excellent Python skills; experience with low-level languages such as Rust or C++ is desirable. - Interpersonal skills: strong communicator who can influence without authority and bridge technical and non-technical audiences, including senior customers. - Education: MSc or equivalent experience required. Desirable: - Edge and embedded deployment: quantisation, pruning, distillation, compilation and runtime optimisation for constrained hardware. - Working with end users: experience at trials, exercises or demonstrations, or working directly with users in operational or other high-stakes settings. - MLOps: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. - Education: PhD in AI/ML/CS or a related field. Beneficial knowledge: - Distributed model training & infra: PyTorch DDP and FSDP, Kubeflow, Slurm, Kubernetes, and a large-scale training framework such as DeepSpeed, Megatron or TorchTitan. - LLM/Agent tooling: evaluation harnesses, prompt optimisation, agent frameworks. - Inference and edge: TensorRT, ONNX Runtime, vLLM, Nvidia Triton Inference Server, KServe, Nvidia Jetson (e.g. Jetson Thor). - Data and platforms: Databricks, Apache Spark, MLflow, SQL, AWS, GCP, Docker.

Discovered 3 days ago

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Lead Machine Learning Engineer

Rowden Technologies · Bristol

LeadHybrid

We’re building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience. We specialise in turning advances in sensing, AI, and communications into o

Skills

Essential: - Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. - People leadership: experience line managing and developing engineers, with a track record of people who got better, took on more, or progressed because of your support. - Growing and resourcing teams: experience hiring during a period of growth, and balancing people across several concurrent projects or customers. - ML engineering: current, hands-on experience building, training, evaluating or deploying ML systems for real-world use, with strong Python and good software engineering habits. - Good engineering practice: a clear view of what good ML and software engineering looks like—performance, architecture, testing, code review, reproducibility, evaluation—and how to build it into a team. - Interpersonal skills: strong communicator who can mentor, influence, handle difficult conversations well, and bridge technical and non-technical audiences. - Education: MSc or equivalent experience required. Desirable: - Breadth across ML: experience with LLMs and agentic systems, edge deployment or large-scale training, enough to support engineers working in those areas. - MLOps: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. Beneficial knowledge: - Ways of working: Agile delivery and engineering career frameworks. - General tooling and platforms: Databricks, AWS, GCP, GitHub, Docker/Kubernetes, MLflow, Jira. - Edge deployments: Nvidia Jetson (e.g. Jetson Thor), Raspberry Pi, or other embedded accelerators.

Discovered 3 days ago

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