Machine Learning SME

We are looking for an experienced MLOps / Machine Learning SME to lead the design, development, deployment, and operationalisation of machine learning solutions across the complete ML lifecycle. The role requires strong hands-on expertise in MLOps, Machine Learning, Python, AWS SageMaker, and AWS Bedrock, along with the ability to provide technical leadership and work directly with clients and cross-functional stakeholders.
The ideal candidate will combine deep technical expertise with strong problem-solving and communication skills to build reliable, scalable, and production-ready machine learning platforms and solutions.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines covering model development, training, deployment, monitoring, and lifecycle management.
• Develop and productionise machine learning solutions using Python and modern ML frameworks.
• Build scalable ML workflows and infrastructure using AWS SageMaker.
• Leverage AWS Bedrock to develop, integrate, and operationalise AI and foundation-model-based solutions.
• Deploy machine learning models into scalable and reliable production environments.
• Implement model monitoring, performance tracking, drift detection, alerting, and continuous improvement processes.
• Develop automated workflows for model training, validation, deployment, and retraining.
• Establish best practices for ML experimentation, versioning, reproducibility, governance, and deployment.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, DevOps, Cloud Architects, and Product teams.
• Troubleshoot complex issues across ML pipelines, infrastructure, deployments, and production environments.
• Optimise ML workloads for performance, scalability, reliability, and cost efficiency.
• Evaluate emerging machine learning and AI technologies and identify opportunities for practical adoption.
• Provide technical guidance and mentorship to engineering and machine learning teams.
• Lead technical discussions, solution reviews, architecture sessions, and client-facing engagements.
• Translate business and client requirements into scalable ML and MLOps solutions.
• Prepare technical documentation, architecture designs, implementation approaches, and operational guidelines.
• Contribute to engineering standards, reusable frameworks, automation, and continuous improvement initiatives.

What Makes You a Great Fit

• 6+ years of experience in Machine Learning, MLOps, ML Engineering, AI Engineering, or a closely related technical field.
• Strong hands-on expertise in end-to-end MLOps and Machine Learning.
• Advanced proficiency in Python for machine learning and production engineering.
• Mandatory hands-on experience with AWS SageMaker.
• Mandatory experience with AWS Bedrock and foundation-model/GenAI solutions.
• Strong understanding of ML model development, deployment, monitoring, and lifecycle management.
• Experience building production-grade ML pipelines and automated model deployment workflows.
• Strong understanding of cloud infrastructure, CI/CD, containers, APIs, and scalable application architectures.
• Experience with model monitoring, observability, model performance, drift, and reliability practices.
• Strong troubleshooting and problem-solving skills across machine learning and cloud environments.
• Proven experience working as a Technical SME, Lead, or senior technical contributor.
• Strong client-facing experience with excellent communication and presentation skills.
• Ability to explain complex ML and MLOps concepts to both technical and non-technical stakeholders.
• Strong stakeholder management and cross-functional collaboration skills.
• Ability to work independently, take ownership of complex technical initiatives, and provide effective technical leadership.
• Experience working in Agile environments and managing multiple priorities effectively.
• A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline is preferred.