Python ML Engineer

Key Responsibilities 1. Incident Management & Bug Fixing Investigate and resolve incidents reported on AI algorithms (model crashes, wrong outputs, data pipeline failures, performance degradation) Diagnose root causes: data issues, code bugs, dependency conflicts, model drift Apply fixes, test and validate corrections before redeployment Document incidents and resolutions in the ticketing system 2. Algorithm Maintenance Apply patches, dependency updates and environment fixes Ensure models remain functional after infrastructure changes (cloud updates, library upgrades, data schema changes) Maintain and update data preprocessing pipelines feeding the models 3. Code Review & Quality Read, understand and improve existing Python codebases (often written by Data Scientists) Refactor poorly structured code to improve maintainability and reliability 4. Collaboration with R&I Teams Experience 3+ years of experience Core Technical Skills — Must Have Domain Skills Level Python Advanced Python, debugging, OOP, clean code- Expert Data manipulation Pandas, NumPy, data wrangling, ETL pipelines - Intermediate ML Frameworks Scikit-learn, TensorFlow or PyTorch (reading & fixing, not training from scratch) -Intermediate API & services FastAPI, REST APIs, JSON, basic microservices -Intermediate Version control Git + GitHub Advanced — see detail below -Intermediate Environment management Poetry, pip, virtual environments, requirements management - Expert Cloud — GCP AI Services GCP Projects, Vertex AI, BigQuery, Service Account, Google Storage (Buckets), Artifact Registry-Beginner Containerisation Docker — run, build, debug a container - Intermediate SQL Ability to query databases and investigate data issues - Intermediate GitHub — Advanced Skills The consultant must demonstrate advanced proficiency with GitHub beyond basic commit/push usage, including: Repository & Code Management Branching strategies: GitFlow, trunk-based development, feature branches Pull Request lifecycle: creating, reviewing, requesting changes, approving and merging Resolving merge conflicts confidently on complex Python codebases Tagging, releases and semantic versioning Collaboration & Governance Branch protection rules: required reviews, status checks before merge Code review best practices: inline comments, suggesting changes directly in PRs Advanced Features GitHub Packages — publishing and consuming internal Python packages Pre-commit hooks and linting integration (Black, Flake8, isort) Git history investigation: git bisect, git blame, git log --graph to trace the origin of a bug