- Company Name
- Fleetiz
- Job Title
- ML Engineer
- Job Description
-
**Job Title:** ML Engineer
**Role Summary:**
Design, develop, and industrialise AI solutions for a SaaS fleet‑management platform, focusing on anomaly detection, fraud prevention, document analytics, and predictive maintenance. Ensure end‑to‑end data pipelines are reliable, models are robust, and deployments meet production performance and reliability standards.
**Expectations:**
- Minimum 5 years of professional experience in machine learning, AI, or data science.
- Proven track record of delivering production‑ready models at scale.
- Strong product‑orientation: translate business needs into technical AI solutions that deliver measurable impact.
**Key Responsibilities:**
- Collaborate with product and engineering teams to prioritise high‑impact AI use‑cases.
- Design, train, validate, and document supervised, unsupervised, and deep‑learning models (e.g., fraud detection, predictive maintenance, document analysis).
- Build and maintain robust, automated ETL pipelines; ensure data quality, normalization, enrichment, and freshness monitoring.
- Deploy models as APIs; optimise inference time and resource usage.
- Monitor model drift, schedule re‑training, and roll‑back when necessary.
- Define success metrics (precision, recall, F1‑score, etc.) and develop rigorous test plans for pre‑deployment validation.
- Identify and mitigate data or model biases.
- Work with backend developers to integrate ML services into the platform stack.
- Evangelise ML best practices and facilitate knowledge transfer within the team.
**Required Skills:**
- Deep expertise in supervised/unsupervised learning and deep‑learning frameworks (PyTorch, TensorFlow).
- Proficient in Python and libraries: scikit‑learn, pandas, NumPy, etc.
- Experience with relational databases (SQL) and handling large datasets.
- Solid understanding of data engineering concepts: ETL, data pipelines, data quality monitoring.
- Familiarity with MLOps practices: CI/CD pipelines, model versioning, API deployment, monitoring.
- Ability to write clear, maintainable code and documentation.
- Strong analytical skills and product‑driven mindset.
**Bonus Skills (Preferred):**
- Fraud detection or predictive maintenance domain experience.
- Computer Vision or NLP for document analytics.
- Hands‑on experience with scaling ML workloads, optimizing inference cost and latency.
**Required Education & Certifications:**
- Bachelor's or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related technical field.
- Relevant certifications (e.g., Azure ML, AWS ML, TensorFlow Developer) are a plus but not mandatory.
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