Lead Platform Engineer

Job description

To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.

Responsibilities

• MLOps Leadership: Build and evolve model deployment pipelines, registries, and monitoring frameworks.
• Tooling & Integration: Implement and optimize SageMaker, Databricks, MLflow, and related orchestration tools.
• Governance & Risk: Ensure compliance with model governance, auditability, and AI safety standards.
• Operational Excellence: Drive reproducibility, lineage tracking, drift detection, and rollback strategies.
• Hands-On Delivery: Write production-grade Python code for automation, orchestration, and monitoring.
• Collaboration: Partner with data scientists, quants, and infra teams to align MLOps capabilities with business needs.
• Mentorship: Coach engineers on best practices for ML lifecycle management and secure deployment.
• Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
• Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
• Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
• Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth.
• Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
• Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.

Requirements

• Strong Python engineering background.
• Deep experience with SageMaker, Databricks, MLflow, and CI/CD for ML.
• Proven track record in operationalizing ML models at scale.
• Knowledge of model governance, compliance, and risk management.
• Familiarity with feature stores, model registries, and observability tools (Langfuse, Observe).
• Kubernetes (EKS/on-prem), Airflow, Step Functions for orchestration.
• Databricks for data engineering and ML pipelines.
• MLflow for experiment tracking and model registry.
• SageMaker for training and deployment.
• Langfuse for evaluation and tracing.