1 to 25 of 72 Permanent MLOps Jobs in London

Experience working with major cloud platforms such as AWS, Azure, or GCP. Familiarity with containerisation and orchestration technologies (e.g. Docker, Kubernetes). Exposure to MLOps practices or deploying AI/ML models into production environments. Experience working in agile or fast-paced delivery teams . ...

learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: Competitive Salary & Bonus : £35,000 – £45,000 plus bonus Hybrid Working : A flexible mix of office ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

solutions based on real user feedback and real-world constraints. Learn modern tooling and practices Gain hands-on experience with ML frameworks, cloud infrastructure, MLOps tools, and best practices for building scalable AI systems. Communicate clearly Translate technical findings into clear, structured insights for collaborators across technical and business teams. ...

fast-paced environment Nice to Have Experience within financial services or other regulated industries Exposure to vector databases (e.g. Pinecone, Weaviate, FAISS) Experience with MLOps/LLMOps and deploying models into production Familiarity with LLM fine-tuning, evaluation, or guardrailing techniques Experience building internal tools such as copilots or knowledge ...

Strong technical skills in Python, Java/Go, TensorFlow, PyTorch, and data engineering. Proven ability to engage directly with CxO-level stakeholders. Experience in MLOps, AI governance, and large-scale deployment. Recognised professional certifications in AI or cloud technologies. If you have these skills and would like to find ...

Owning ML models and pipelines that extract structured knowledge from complex, unstructured documents at scale. ▸ NLP · Document AI · Data Pipelines · Evals 3. Staff SE – MLOps/DevOps for AI Shaping the infrastructure behind model serving, training pipelines, LLMOps, and AgentOps across the full AI stack. ▸ MLFlow · Databricks · Datadog · Terraform ────────────────────────── What ...

LLMs, prompt engineering, RAG, vector databases, evaluation, and security. Hands-on experience with fine-tuning, deploying, and monitoring large-scale GenAI systems. Strong MLOps/LLMOps capability: CI/CD, monitoring, governance. Cloud expertise across AWS, Azure, and/or GCP (cloud-agnostic preferred). Ability to communicate complex ...

model artifacts, and inference outputs. Maintain audit trails for model decisions, data access, and deployment history. Attributes Required Qualifications 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related infrastructure role. Strong proficiency in Python for ML pipeline development, tooling, and automation. Hands-on experience with ...

Azure or Google Cloud** Data platforms including **Databricks, Snowflake or BigQuery** Python and modern ML frameworks Generative AI, LLM integration and RAG pipelines MLOps tooling and modern ML lifecycle management. The profile Experience designing enterprise AI or data platforms in complex technology environments. A background delivering large-scale transformation programmes ...

ecosystem components including data lakes, data warehouses, data mesh architectures, and associated architectural patterns. Strong understanding of methodologies and principles of data science and MLOps, including model development, validation, and lifecycle management. Experience in data pipeline design, ETL/ELT processes, and data integration approaches and appreciation of trade-offs ...

Solid understanding of ETL and data engineering best practices Experience with SQL/NoSQL, APIs, and cloud platforms Familiarity with big data tools and MLOps concepts Strong communication, collaboration, and mentoring skills Main Tech Stack: Python AWS (S3, Athena), Terraform, Docker Prefect, Pulsar PostgreSQL, Cassandra GitLab CI/CD What ...

systems . Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment. Optimise model performance, scalability, and reliability using MLOps best practices. Work with large-scale structured and unstructured datasets for model training and validation. Implement model monitoring, versioning, and retraining processes to ensure continuous … TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with ...

product-focused mindset, balancing experimentation with real-world value. Useful additional experience includes: AWS or GCP environments (SageMaker, Bedrock, Lambda, S3, Redshift). MLOps practices (monitoring, deployment, optimisation). Working with streaming data, transformer models or recommendation systems. What They Offer £70,000 to £90,000 salary. Hybrid working with ...

/ML initiatives, or operating model design A solid understanding of modern data architectures (data lakes, warehouses, data mesh) Knowledge of data science, MLOps, and advanced analytics workflows Experience with cloud platforms such as Azure, AWS, or GCP (e.g. Snowflake, Databricks) Proven ability to lead or contribute to large-scale ...

powered features to production. Proactively detect, troubleshoot, and resolve infrastructure and model performance issues. Stay up to date with industry best practices in DevOps, MLOps, and infrastructure engineering. Document infrastructure, workflows, and operational procedures clearly and thoroughly. We’re Looking For: Proven experience in a senior-level DevOps, MLOps ...

strategies proactively Support documentation of architecture, pipelines, and operational processes Good to Have Exposure to AI/ML data workflows (feature engineering, model inputs, MLOps basics) Awareness of LLMs/Agentic AI architectures from a data platform perspective Experience with other platforms such as AWS, GCP, Snowflake, BigQuery, Redshift Familiarity ...

architectural decisions that balance speed, quality, and regulatory compliance Also Valuable Experience with healthcare data interoperability (FHIR, HL7, DICOM) Exposure to ML model serving, MLOps pipelines, or ML frameworks (TensorFlow, PyTorch) Understanding of GDPR, HIPAA, or UK Data Protection Act compliance Experience with Infrastructure as Code (Terraform, CloudFormation ...

PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. Experience configuring and maintaining cloud infrastructure including network infrastructure, compute, access control policies, load balancers, Public ...

deal requirements into technical solutions. Support post‐merger integration (PMI) through AI‐enabled insights, reporting, and performance tracking. Deploy solutions into production using modern MLOps and cloud practices . Ensure models are explainable, auditable, and aligned with governance and regulatory requirements in transaction contexts. Contribute to AI strategy, architecture … Spark, Airflow or similar) LLMs, NLP, and document intelligence (e.g. RAG, embeddings) Experience with cloud platforms (AWS, Azure, or GCP). Understanding of MLOps , CI/CD, model monitoring, and governance. M&A/Transaction Experience Direct experience supporting M&A, private equity, corporate development, or transaction advisory . Proven ...

Senior ML/MLOps Engineer (MLflow) | Databricks | Azure) About Optima Partners We’re an advanced data & business consultancy helping major consumer brands transform through data-driven customer strategy, design, and engineering. Our clients include leading UK and global brands across financial services, retail, and more. Join a practitioner-led, fast … growing team delivering ML systems that actually make it into production. We’re growing our engineering team and looking for an MLOps Engineer who loves building real, production grade ML systems not just proof‐of‐concepts. If you enjoy working across the full ML lifecycle and have strong MLflow experience ...

product and systems thinking with a practical, impact-led mindset. Nice to Have AWS experience (e.g. SageMaker, Bedrock, Lambda, S3, Redshift). Familiarity with MLOps practices. Background in adtech, media, fraud, or adversarial systems. Why Join Work on technically challenging, high-scale AI problems. End-to-end ownership from experimentation ...

operate and maintain AI development and production environments using Azure services, including AI, ML and GenAI workloads. Design and manage CI/CD and MLOps pipelines to support model development, deployment, monitoring and lifecycle management. Provision and secure cloud environments using Infrastructure as Code, with a strong focus on automation ...

related field Experience with genomics pipelines or tools (e.g. FASTQ, BAM, variant calling) Knowledge of protein structure modelling or drug discovery workflows Exposure to MLOps, deployment, and production systems Publications or open-source contributions in relevant domains Why Join Them Opportunity to work on genuinely impactful problems in healthcare ...

needed for this role: Expert AI/ML Skills Strong in both traditional ML and modern LLM/RAG/agentic workflows Production Deployment & MLOps Comfortable shipping scalable AI systems using APIs, microservices, Docker/Kubernetes, click apply for full job details ...