- Company Name
- Hiscox
- Job Title
- Lead ML Engineer
- Job Description
-
**Job Title:** Lead Machine Learning Engineer
**Role Summary:**
Leads the Machine Learning Engineering sub‑chapter, managing a team of ML Engineers and partnering with Data Science and Platform groups to deliver scalable, secure, and production‑ready machine learning solutions. Owns the MLOps platform, sets engineering standards, and drives capability development across the organization.
**Expectations:**
- Provide people leadership: set objectives, conduct performance reviews, mentor engineers.
- Define and execute chapter strategy aligned with organizational goals.
- Ensure reliability, security, and scalability of the MLOps platform.
- Establish governance, guardrails, and best‑practice standards for production ML.
- Collaborate with Data Science and delivery squads to transition models from experimentation to production.
**Key Responsibilities:**
1. **People Leadership** – Grow talent, foster a collaborative engineering culture, and guide career progression.
2. **Strategic Capability Development** – Shape technical direction, establish ML engineering standards, and drive upskilling initiatives.
3. **Platform Ownership** – Own, evolve, and secure the MLOps platform; enable reusable, cross‑stream ML delivery.
4. **Technical Enablement** – Lead spikes and PoCs, de‑risk architectural decisions, evaluate emerging tools.
5. **Governance & Standards** – Define compliance, security, and operational guardrails; oversee model deployment, monitoring, retraining, and decommissioning.
6. **Collaboration & Influence** – Partner with Data Science sub‑chapters, represent ML Engineering in strategic forums, advocate for tooling and scalable practices.
**Required Skills:**
- Strong Python programming with solid OOP, testing, and design‑pattern fundamentals.
- Proven experience delivering production ML systems at scale (deployment, monitoring, maintenance).
- Hands‑on cloud expertise (AWS, GCP, or Azure) including containerised deployments.
- MLOps proficiency: CI/CD pipelines, Git workflows, IaC (e.g., Terraform).
- Operational excellence: API management, logging, monitoring, reliability of ML services.
- SQL and data integration knowledge.
- Ability to evaluate commercial impact of ML models in production.
- Effective collaboration in Agile, cross‑functional squads.
- Demonstrated line‑management or technical mentorship experience.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Engineering, a quantitative field, or equivalent practical experience.
- Relevant certifications (e.g., cloud provider certifications, MLOps/DevOps certifications) are advantageous but not mandatory.