- Company Name
- Financial Ombudsman Service
- Job Title
- Machine Learning Engineer
- Job Description
-
Job title: Machine Learning Engineer
Role Summary: Lead the design, development, and production deployment of ML and Generative AI solutions. Build scalable data pipelines, manage LLM-based automations, and ensure ML Ops excellence across architecture and cloud environments. Collaborate with cross-functional teams to translate business needs into technical designs and deliver high‑quality models that enhance customer journeys.
Expectations:
- Deliver end‑to‑end ML projects from concept to production on time and within scope.
- Own user stories, estimate effort, and provide trade‑off analyses for cost and performance.
- Communicate results to technical and non‑technical stakeholders clearly and effectively.
- Continuously improve pipelines, models, and deployment practices to meet evolving business needs.
Key Responsibilities:
- Clarify requirements, estimate effort, and contribute to sprint planning and backlog refinement.
- Design and develop Gen AI application stacks, including LLM integration and automation workflows.
- Build, maintain, and optimise machine learning pipelines and data transformation workflows on Azure (preferred), AWS, or GCP.
- Implement CI/CD and ML Ops pipelines using Azure DevOps, GitHub Actions, Jenkins, Bicep/Terraform, and Azure AI services.
- Monitor deployed models, troubleshoot issues, and perform iterative improvements.
- Collaborate with architects, senior developers, product owners, and analysts on solution design and architecture decisions.
- Document solutions, model rationales, and operational guidelines for knowledge transfer.
Required Skills:
- Proven experience leading full‑cycle ML projects into production.
- Proficient in Python; strong grasp of programming concepts, algorithms, and cloud fundamentals.
- Solid background in ML techniques (regression, clustering, tree‑based methods, reinforcement learning, CNN, RNN, LSTM, attention, Transformers, vector semantics).
- Experience with serverless pipeline development on Azure (preferred) or AWS/GCP and data warehousing (SQL & NoSQL).
- Knowledge of architecture frameworks, patterns, and design principles.
- Ability to explain complex technical concepts to diverse audiences.
- Familiarity with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins) and IaC (Bicep, ARM templates, Terraform).
- Experience with Gen AI frameworks such as LlamaIndex, LangChain is desirable.
Required Education & Certifications:
- Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, or equivalent field.
- Professional certifications in cloud platforms (Azure, AWS, or GCP) and/or ML Ops are advantageous.