- Company Name
- Urban Connect
- Job Title
- Lead MLOps Engineer (Databricks/AWS/SageMaker/Python) - Fully remote - OUTSIDE IR35
- Job Description
-
**Job title**
Lead MLOps Engineer (Databricks → AWS SageMaker)
**Role Summary**
Contract Lead MLOps Engineer responsible for end‑to‑end migration of production ML workloads from Databricks to AWS SageMaker in a regulated environment. Provides technical leadership, defines operating model, ensures delivery quality, and bonds cross‑functional workstreams.
**Expectations**
* Deliver a container‑first migration within a hard commercial deadline.
* Own technical direction, quality, and coordination of all migration workstreams.
* Lead and unblock team members; handle complex edge‑case workloads.
* Transition the client to a SageMaker‑centric MLOps model for future business‑as‑usual.
**Key Responsibilities**
* Design, test, and maintain SageMaker batch training, inference, and auto‑scaling pipelines.
* Migrate Databricks notebooks, jobs, and ML workloads to containerised SageMaker execution, preserving behavioural parity.
* Refactor Python ML code (sklearn, XGBoost, etc.) to support containerised runs and dependency management.
* Build and manage Docker image stacks that replicate Databricks runtimes.
* Orchestrate end‑to‑end workflows: data ingestion, model training, retraining triggers, validation, and deployment.
* Implement logging, monitoring, alerting, and governance for regulated production environments.
* Produce technical documentation and best‑practice guidelines for the new operating model.
* Collaborate with Data Engineers, Cloud Engineers, Delivery Management, and Data Science SMEs.
* Lead technical reviews, code quality checks, and risk assessments.
* Provide status updates to stakeholders and ensure adherence to project timelines.
**Required Skills**
* Proven migration experience from Databricks to AWS SageMaker (minimum 1–2 successful projects).
* Strong Python (≥3.8) expertise: sklearn, XGBoost, pandas, NumPy.
* Containerisation skills: Docker, Kubernetes or ECS/EKS, image signing, CI/CD pipelines.
* AWS proficiency: SageMaker, S3, IAM, CloudWatch, CloudFormation/CLOUDFORMATION.
* Familiarity with ML workflow orchestration (Airflow, Step Functions, or similar).
* Experience in regulated or high‑governance domains (e.g., finance, healthcare, pharma).
* Ability to define and enforce audit‑ready logging, metrics, and validation.
* Leadership: experience leading mixed technical teams, delivering on schedule.
* Strong communication and documentation abilities.
**Required Education & Certifications**
* Bachelor’s (or higher) degree in Computer Science, Data Engineering, or related field.
* AWS Certified Machine Learning – Specialty or equivalent.
* (Optional) Databricks Certified Professional — Databricks Delta Lake or AWS Certified Solutions Architect – Associate.