Company Overview
We are partnering with a leading banking client to build next-generation AI and machine learning platforms. You will join a high-impact agile team within the ML & AI domain, working on production-grade systems that power intelligent, scalable digital solutions.
Job Description
We are seeking skilled Machine Learning Platform Engineers (MLOps / AI Engineers) to bridge the gap between experimental data science and production-ready systems.
In this role, you will contribute across the full ML lifecycle - from concept to deployment - and work closely with data scientists, engineers, and infrastructure teams to deliver robust AI-driven solutions. You will also support the development of agentic AI workflows and LLM-powered systems, enabling scalable and autonomous capabilities.
Key Responsibilities
- Design, develop, and deploy scalable machine learning solutions and services
- Build and maintain end-to-end ML pipelines (data ingestion, training, validation, deployment, monitoring)
- Operationalise LLMs, embeddings, and multi-agent systems in production environments
- Design and implement Retrieval-Augmented Generation (RAG) systems
- Manage full model lifecycle (experimentation, model registry, deployment, monitoring)
- Oversee model promotion processes, including validation gates and approval workflows
- Containerise applications using Docker and orchestrate using Kubernetes (K8s)
- Build and maintain CI/CD pipelines for ML and AI applications
- Collaborate with data scientists to productionise research code into scalable Python applications
- Monitor model performance, data drift, and system reliability in production
- Integrate AI solutions into existing enterprise systems and infrastructure
- Participate in code reviews, testing, and debugging to ensure high-quality deliverables
Requirements
Education
- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, or related field
Technical Skills
- Strong proficiency in Python (clean, testable, efficient code)
- Experience with Docker and working knowledge of Kubernetes
- Hands-on experience with MLOps tools (e.g., MLflow, Weights & Biases)
- Experience working with Large Language Models (LLMs)
- Understanding of AI agents, agentic workflows, and LLM orchestration frameworks
- Experience with data preprocessing, feature engineering, and model evaluation
- Familiarity with CI/CD tools (e.g., GitLab, Jenkins)
- Understanding of GPU computing and cloud-based ML infrastructure
- Strong foundation in statistics, probability, and linear algebra
Experience
- Relevant experience in machine learning, data science, or MLOps roles
- Experience deploying models into production environments is highly preferred
Competencies
- Strong analytical and problem-solving skills
- Ability to work in a fast-paced, agile environment
- Excellent communication and stakeholder management skills
- Strong attention to detail and code quality
- Self-driven with a continuous learning mindset
