Staff Engineer (AI ML Engineer)

Must have Skills : Databricks ML Engineering & MLOps Critical Azure Kubernetes Critical GenAI / LLM Engineering 

Job Purpose
To design prototype and build next-generation analytic engines and services by applying strong expertise in Artificial Intelligence (AI)

Purpose
Build deploy scale and support machine learning AI and GenAI solutions in production. The role focuses on operationalising models developing AI applications and agents and creating the platforms and services required to deliver business value at scale.

Key Responsibilities

• Productionise deploy and monitor machine learning models and data science pipelines on Databricks.
• Build deploy and support AI Agents GenAI applications and RAG solutions on Databricks.
• Develop and maintain reusable ML pipelines using MLOps principles including CI/CD automated testing monitoring and governance.
• Deploy optimise and manage open source AI and machine learning models on Azure Kubernetes Service (AKS).
• Design develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications.
• Implement containerised solutions using Docker and Kubernetes to ensure scalable secure and resilient deployments.
• Monitor model performance drift reliability and operational health in production environments.
• Partner with Data Scientists to productionise prototypes and enable business-ready solutions.
• Collaborate with platform security cloud and infrastructure teams to ensure compliance with enterprise standards.
• Troubleshoot and resolve production issues related to models pipelines APIs and AI applications.
• Optimise AI and ML solutions for performance scalability cost and reliability.
• Contribute to engineering standards reusable frameworks and best practices across the AI and ML ecosystem.
• Mentor junior engineers and promote knowledge sharing across the team.
• Stay current with advancements in AI GenAI MLOps Databricks Kubernetes and cloud technologies.

Core Deliverables

• Production-ready ML models and pipelines running on Databricks.
• AI Agents and business applications deployed on Databricks.
• Open source LLMs and AI services deployed on AKS.
• Secure and scalable APIs exposing AI capabilities to consuming systems.
• Automated deployment monitoring and governance processes.
• Reliable scalable and compliant AI platforms supporting business outcomes.

Key Skills

• Databricks Workflows Model Serving MLflow and Mosaic AI
• Azure Kubernetes Service (AKS)
• Python SQL and REST APIs
• Docker and Kubernetes
• CI/CD and MLOps practices
• Machine Learning and Generative AI
• LLM deployment and optimisation
• Cloud engineering and infrastructure automation
• Monitoring observability and troubleshooting

Computer Science Engineering Econometrics Mathematical Statistics Actuary Science. Masters or Doctorate will be an added advantage.

Preferred Certifications

• Microsoft Azure certifications (AZ-104 AZ-305 AI-102 or equivalent)
• Databricks certifications (Data Engineer Machine Learning Engineer Generative AI Engineer)
• Kubernetes and containerisation certifications (CKA CKAD or equivalent)
• DevOps MLOps or Platform Engineering certifications
• AWS or Google Cloud certifications will be advantageous
• Machine Learning Artificial Intelligence or Data Science certifications from recognised providers such as Microsoft Databricks SAS Coursera or will be an added advantage

Technical / Professional Knowledge

• Strong understanding of MLOps DevOps and software engineering practices for machine learning platforms.
• Experience building deploying and supporting machine learning solutions in production environments.
• Proficiency in Python and experience with SQL and API development.
• Experience with Databricks MLflow Model Serving and cloud-native AI/ML platforms.
• Hands-on experience with Kubernetes Docker and containerised application deployment.
• Experience deploying and supporting machine learning and Generative AI solutions on Azure Kubernetes Service (AKS).
• Knowledge of CI/CD pipelines infrastructure automation and platform monitoring.
• Experience with distributed computing technologies such as Spark and large-scale data processing frameworks.
• Understanding of machine learning large language models (LLMs) retrieval-augmented generation (RAG) and AI agents.
• Ability to productionise data science solutions and collaborate effectively with Data Scientists.
• Experience delivering end-to-end AI and machine learning use cases from development to production.
• Ability to translate technical concepts into business outcomes and communicate effectively with stakeholders.
• Strong written and verbal communication skills with the ability to work across cross-functional teams.
• Self-driven adaptable and capable of thriving in a fast-paced technology-driven environment.

Remote Work :

No

Employment Type :

Full-time