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

- 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 Setting 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

📌 Python ML Engineer (Kochi)
🏢 Astek
📍 Kochi