Senior Machine Learning Engineer
Strong software engineering experience delivering end-to-end services in production (not just notebooks/experiments), with ownership for run/support considerations Proficiency in Python and modern engineering practices (clean code, testing, packaging, dependency management, Git-based workflows) Hands-on experience with AI deployment patterns and infrastructure (e.g. containerisation with Docker, orchestration such as Kubernetes, API-based serving, batch/stream inference) Practical MLOps experience: CI/CD for ML, model packaging and release management, automated validation, monitoring, and lifecycle management Working knowledge of ML/DL frameworks and tooling (e.g.
PyTorch/TensorFlow and the Python ML ecosystem) sufficient to collaborate effectively with data scientists and implement inference pipelines Experience working with complex, multi-layered datasets (including imbalanced data) and integrating data pipelines into AI services Hands-on experience building and deploying web APIs using libraries such as Flask or FastAPI. Proficiency with database technologies such as SQL Server or Postgres, etc.
Strong stakeholder communication skills: able to explain technical designs, risks, and operational considerations to wide-ranging audiences Good organisational skills and delivery discipline (prioritisation, time management, working across multiple initiatives) Proven track record designing, deploying and operating production ML/GenAI services in cloud environments, understanding the operational realities vs on‑prem (security, networking, scaling, resilience and cost management).
Hands on experience building agentic LLM systems, including RAG workflows, tool/function calling and orchestration, and integrating external APIs and enterprise data sources to improve business operations with appropriate guardrails and evaluation.
