- Company Name
- SPG Resourcing
- Job Title
- Lead Machine Learning Engineer
- Job Description
-
Job title: Lead Machine Learning Engineer
Role Summary
Senior-level engineering leader responsible for developing and scaling production‑grade machine learning systems. Owns MLOps platform strategy, guides a team of ML engineers, and ensures seamless transition of models from experimentation to reliable deployment.
Expectations
- Lead technical vision for ML engineering aligned with business objectives.
- Mentor and grow a cross‑functional engineering team.
- Champion best practices, governance, and operational excellence across ML lifecycles.
Key Responsibilities
- Manage, coach, and evaluate a team of ML engineers, setting goals and performance reviews.
- Define and evolve ML engineering strategy, standards, and roadmaps for deployment, infrastructure, and MLOps.
- Own the end‑to‑end MLOps platform: reliability, scalability, security, and cost efficiency.
- Drive capability development in cloud platforms, software engineering, and MLOps tools.
- Conduct technical architecture reviews, proof‑of‑concepts, and pilot projects.
- Ensure compliance with security, architecture, and operational policies for ML systems.
- Establish monitoring, retraining, deployment pipelines, and lifecycle guardrails for production models.
- Partner with data scientists, platform engineers, and senior stakeholders to align solutions with enterprise needs.
- Represent ML engineering in strategic technology discussions and influence tooling decisions.
Required Skills
- Deep expertise in supervised/unsupervised learning, feature engineering, model evaluation, and commercial impact assessment.
- Proven experience leading or mentoring engineering teams and setting technical standards.
- Ownership of or extensive experience with MLOps platforms (e.g., MLflow, Kubeflow, SageMaker) and critical ML infrastructure.
- Strong programming in Python, knowledge of data pipelines, version control (Git), and CI/CD.
- Familiarity with cloud services (AWS, GCP, Azure), containerisation (Docker), orchestration (Kubernetes), and monitoring tools.
- Excellent communication, collaboration, and Agile delivery skills.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related quantitative discipline (or equivalent practical experience).
- Certifications in cloud (AWS/Azure/GCP), MLOps, or advanced data science are preferred.