Simelabs - 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.

Experience :

• 3 years of experience.

Core Technical Skills :

• 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) (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).

3.

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).