Job Title Machine Learning Engineer

Purpose

To lead the design and productionisation of Generative AI and LLM-driven applications, utilising GCP and Google’s AI ecosystem. The ML Engineer will architect scalable systems, mentor junior engineers and drive the technical roadmap for integrating Large Language Models into core products, demonstrating innovation and technical excellence across the company’s AI initiative. They will lead and execute elements of the product road map on time and in line with sprint planning deadlines.

Reports to

Senior Lead Engineer with Line management responsibility

Key Relationships ● Engineering Team ● Product Team

Main Tasks

General

1 Design, develop and deploy advanced machine learning and deep learning models and pipelines.

2 Collaborate with cross-functional teams to translate complex business problems into ML-driven solutions.

3 Architect scalable LLM Ops pipelines on GCP (Vertex AI) for model fine-tuning, vector retrieval and low-latency inference.

4 Implement robust LLM evaluation frameworks to monitor output quality (hallucinations, relevance), manage token costs and ensure content safety/governance.

5 Utilise MLOps tools and Google’s Agent Development Kit (ADK) to design autonomous agents and optimise deployment workflows on Vertex AI.

6 Lead the end-to-end development of LLM applications, utilising RAG (Retrieval-Augmented Generation), Agentic workflows and prompt engineering strategies.

7 Monitor, optimize, and fine-tune models in production environments.

8 Contribute to the architecture and design of the company’s AI platform and data infrastructure.

9 Stay up to date with the latest research, emerging technologies, and industry trends in AI and ML

Human Resources

10 Mentor junior engineers and data scientists, promoting best practices in ML development.

Health & Safety

11 To comply with allocated mandatory training

12 To ensure the timely reporting and of accidents (including RIDDOR) and near misses and support appropriate investigation

13 To follow company guidance and policy to reduce risk

Other

14 Perform any other tasks/duties requested by the Engineering Lead

15 To represent the company in a professional manner at all times

Person Specification

Essential Desirable Qualifications Master’s or PhD in Computer

Science, Artificial Intelligence, or related field.

Related research publications.

Experience Experience in machine learning engineering or applied AI. Recent demonstrable experience with LLMs, RAG or generative AI models.

Fine-tuned models using standard frameworks. Experience of Reinforcement Learning approaches.

Strong programming expertise in Python, including TensorFlow, PyTorch, Google GenAI or Hugging Face.

Proven experience building autonomous agents using Google ADK or similar Agentic frameworks

Experience with vector databases and retrieval-augmented architectures.

Proficiency with Prompt Engineering and Evaluation frameworks (e.g., RAGAS, TruLens) for testing LLM outputs.

Deep expertise in GCP Vertex AI (preferred) or equivalent cloud platforms..

Background in mathematics, statistics, or theoretical computer science

Deep understanding of APIs, containerization (Docker), and CI/CD pipelines.

Experience with version control (Git) and collaborative development environments.

Experience architecting and maintaining large-scale ML systems

Strong understanding of statistical modeling, deep learning, and optimization techniques

Technical Skills Expertise in deep learning frameworks (TensorFlow, PyTorch).

Proficiency in Agentic Design Patterns (ReAct, Chain-of-Thought, Tool-use) and state management for conversational AI.

Strong software engineering background (Python, C++, or Java).

Deep expertise in GCP Vertex AI (preferred) or equivalent cloud platforms..

Understanding of data governance, bias mitigation, and model interpretability techniques.

Soft Skills Strategic thinker and technical leader.

Adaptability to fast-evolving AI technologies

Ability to work independently and with ambiguity in addressing complex problems

Ability to define, set and manage own work aligned with the overall product road map and strategic direction

Excellent mentorship and communication abilities.

Strong collaboration with business and engineering teams.