Required Skills: Python, Fast API, Flask, ML Pipeline

Job Description

Top 3 Skills:

• Production‑grade machine learning system development
• Strong Python and ML frameworks (scikit‑learn, PyTorch, TensorFlow, XGBoost)
• Data engineering and ML pipeline development

Detailed Responsibilities:

Model Development & Applied AI
• Partner with stakeholders to frame business problems as ML/AI use cases, define success metrics, and identify required data
• Build and iterate on models (classification, regression, ranking, clustering, anomaly detection, NLP)
• Perform feature engineering, dataset creation, labeling strategies, and model evaluation with strong rigor
• Implement model interpretability, bias assessment, and responsible AI practices

Production Engineering & MLOps
• Build production-grade ML services and pipelines (batch and real-time)
• Deploy models using CI/CD and infrastructure-as-code practices
• Monitor data drift, model drift, latency, throughput, cost, and model quality
• Maintain versioning of datasets, features, models, and experiments

Data & Platform Collaboration
• Collaborate with data engineering to build and maintain robust pipelines
• Work with software engineers to integrate ML models via APIs, event streams, or workflows
• Document architecture, runbooks, and model artifacts; participate in technical reviews

Required Skills & Technologies:

• 3–5+ years of experience deploying ML systems to production
• Strong Python programming and ML libraries (scikit‑learn, PyTorch, TensorFlow, XGBoost)
• Data processing experience with Pandas, NumPy, SQL, and Spark (as applicable)
• Experience building ML services (FastAPI or Flask), containerization (Docker), and CI/CD
• Experience developing APIs and integrating with third‑party APIs (4+ years)
• Strong SQL skills (SQL Server or Oracle)
• Familiarity with scripting (Python, Bash, PowerShell)
• Version control experience (Git, GitHub, GitLab)
• Understanding of DevOps and CI/CD best practices