- Type
- Full-time
- Department
- IT
- Education
- PhD
- Visa
- Not sponsored
- Closing date
- Today
- Source
- Vincere
Description
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.
