AI Summary
Lead the development of scalable AI/ML solutions, design robust ML pipelines, and spearhead projects across key AI domains.
Key Highlights
Lead the end-to-end development of scalable AI/ML solutions
Design, build, and maintain robust ML pipelines and APIs
Spearhead projects across key AI domains, including NLP, Computer Vision, and Generative AI
Technical Skills Required
Benefits & Perks
£90,000 per annum
Fully Remote (UK-Based)
Job Description
Senior Machine Learning Engineer (AI/ML Focus)
Work Mode: Fully Remote (UK-Based)
Salary Package: £90,000 per annum
Key Responsibilities
- Lead the end-to-end development of scalable AI/ML solutions—from research and prototyping to deployment and monitoring—solving complex business problems.
- Design, build, and maintain robust ML pipelines and APIs, ensuring seamless integration of models into production systems and applications.
- Spearhead projects across key AI domains, including NLP, Computer Vision, and Generative AI (e.g., LLMs, RAG applications), to deliver measurable impact.
- Establish and champion MLOps best practices, leveraging tools like Docker, Kubernetes, MLflow, and Azure to ensure model reproducibility, scalability, and automated CI/CD workflows.
- Collaborate cross-functionally with Data Engineering, Product, and DevOps teams to align AI initiatives with business goals and architectural standards.
- Mentor junior data scientists/engineers and contribute to the team's technical strategy and knowledge base.
- 5+ years of professional experience in building, deploying, and maintaining production-grade AI/ML systems.
- Expert proficiency in Python and core ML libraries (PyTorch/TensorFlow, scikit-learn, pandas). [Note: Removed Django/Flask from "strong proficiency" - see below].
- Proven hands-on experience in at least two of the following: Deep Learning, NLP, Computer Vision, or Generative AI/LLM applications.
- Solid experience with modern MLOps tooling: Docker, Git, CI/CD, and at least one major cloud platform (Azure preferred; AWS/GCP acceptable).
- Strong foundation in software engineering for production: version control, testing, APIs, and familiarity with databases (SQL).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related quantitative field, or equivalent practical experience.
- Direct experience with Generative AI tech stacks (e.g., LangChain, RAG architectures, LLM fine-tuning and deployment) is a significant advantage.
- Experience with data pipeline/orchestration tools (Apache Spark, Airflow, dbt).
- Knowledge of container orchestration (Kubernetes) and model lifecycle management (MLflow).
- A portfolio of projects, contributions to open-source ML projects, or relevant publications.