AI & MLops Engineer
Posted 1 week ago by Eden Smith Limited
Negotiable
Undetermined
Undetermined
London OR Warrington , UK
Summary: This role is for an AI/Machine Learning Engineer focused on deploying and optimizing Large Language Models (LLMs) and machine learning solutions in a cloud-native environment. The engineer will build production-ready ML-Ops infrastructure, emphasizing Databricks and scalable ML workflows. Key responsibilities include developing MLOps pipelines and collaborating with various teams to enhance operational efficiency through AI and data products. The position requires deep expertise in Databricks, MLOps, and unstructured data processing.
Key Responsibilities:
- Design, build, and deploy machine learning models using Databricks, cloud ML services, and modern LLM tooling.
- Implement, optimise, and scale LLMs for a range of enterprise applications.
- Develop robust MLOps pipelines to support end-to-end model life cycle management, including experimentation, CI/CD, deployment, monitoring, and governance.
- Build workflows for processing and analysing unstructured data (text, documents, audio, image, etc.).
- Collaborate with data scientists, data engineers, platform teams, and product stakeholders to operationalise ML solutions.
- Monitor and troubleshoot production models to ensure performance, reliability, and ongoing optimisation.
- Design and integrate agentic AI workflows and autonomous orchestration solutions using modern LLMOps frameworks.
- Maintain clear documentation of models, workflows, and operational processes.
- Stay current with advancements in LLMs, MLOps, distributed compute, and cloud-native AI tooling.
Key Skills:
- Extensive hands-on experience with Databricks, including model development, data engineering workflows, and ML runtime environments.
- Strong background in MLOps, including MLflow, CI/CD, model registry management, experiment tracking, and scalable deployment strategies.
- Proven experience working with unstructured data and building pipelines to extract, transform, index, and analyse it.
- Strong knowledge of LLMOps practices across deployment, monitoring, optimisation, and governance.
- Proficiency in Python, PyTorch, and modern LLM frameworks (eg, LangChain, LangSmith).
- Experience deploying cloud-native ML systems, including containerisation (Docker) and orchestration (Kubernetes).
- Solid understanding of cloud compliance, governance, and core cloud services (eg, VMs, identity management, automation).
- Experience building ETL/ELT workflows using platforms such as Databricks pipelines or cloud data factory tools.
- Proficiency with Git-based version control and CI/CD pipelines.
- Comfortable working in Agile product teams, participating in stand-ups, sprint planning, and retrospectives.
- Strong communication skills and a collaborative mindset.
Salary (Rate): £600/day
City: London
Country: UK
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Job Title: AI/Machine Learning Engineer
Location: UK
Contract Type: Contractor
Travel: Occasional travel as required
Role Purpose
This role focuses on deploying, managing, and optimising Large Language Models (LLMs) and other machine learning solutions within a cloud-native environment. The successful candidate will build production-ready ML-Ops infrastructure, with a strong emphasis on Databricks, scalable ML workflows, and the effective use of cloud resources.
A critical requirement is deep, hands-on expertise with Databricks, MLOps, and unstructured data processing. The engineer will contribute to the development of AI and data products that drive operational efficiency, enhance decision-making, and support intelligent automation across the organisation.
Key Responsibilities
- Design, build, and deploy machine learning models using Databricks, cloud ML services, and modern LLM tooling.
- Implement, optimise, and scale LLMs for a range of enterprise applications.
- Develop robust MLOps pipelines to support end-to-end model life cycle management, including experimentation, CI/CD, deployment, monitoring, and governance.
- Build workflows for processing and analysing unstructured data (text, documents, audio, image, etc.).
- Collaborate with data scientists, data engineers, platform teams, and product stakeholders to operationalise ML solutions.
- Monitor and troubleshoot production models to ensure performance, reliability, and ongoing optimisation.
- Design and integrate agentic AI workflows and autonomous orchestration solutions using modern LLMOps frameworks.
- Maintain clear documentation of models, workflows, and operational processes.
- Stay current with advancements in LLMs, MLOps, distributed compute, and cloud-native AI tooling.
Skills & Experience
Essential:
- Extensive hands-on experience with Databricks, including model development, data engineering workflows, and ML runtime environments.
- Strong background in MLOps, including MLflow, CI/CD, model registry management, experiment tracking, and scalable deployment strategies.
- Proven experience working with unstructured data and building pipelines to extract, transform, index, and analyse it.
- Strong knowledge of LLMOps practices across deployment, monitoring, optimisation, and governance.
- Proficiency in Python, PyTorch, and modern LLM frameworks (eg, LangChain, LangSmith).
- Experience deploying cloud-native ML systems, including containerisation (Docker) and orchestration (Kubernetes).
- Solid understanding of cloud compliance, governance, and core cloud services (eg, VMs, identity management, automation).
- Experience building ETL/ELT workflows using platforms such as Databricks pipelines or cloud data factory tools.
- Proficiency with Git-based version control and CI/CD pipelines.
- Comfortable working in Agile product teams, participating in stand-ups, sprint planning, and retrospectives.
- Strong communication skills and a collaborative mindset.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML Engineering, or a related field.
- 5+ years of hands-on experience in machine learning engineering and data engineering.
- Demonstrated experience with Databricks and cloud-based ML services.