Machine Learning Engineer

ML Engineer

Location: Hybrid

Employment Type: Full-time

Roles & Responsibilities

• Model Development: Design, build, and deploy machine learning models across classical, time-series, and deep learning approaches to solve real business problems.
• Data Pipelines: Build and maintain reliable data ingestion and transformation pipelines, ensuring data quality, consistency, and reproducibility.
• Feature Engineering: Engineer high-quality features from structured and unstructured data, and manage them through feature stores for training and serving consistency.
• Experimentation: Run rigorous experiments using proper validation strategies, perform hyperparameter tuning, and benchmark models against well-defined baselines.
• Model Deployment: Package models into containerised services, expose them via APIs, and deploy to cloud ML platforms for scalable, low-latency inference.
• MLOps & Automation: Implement CI/CD for ML, automated retraining workflows, model registry, and champion–challenger promotion to keep models production-ready.
• Monitoring & Validation: Track production model performance, detect data and concept drift, and continuously validate accuracy against business KPIs.
• Collaboration: Partner with data engineers, software engineers, analysts, and business stakeholders to translate problems into ML solutions and explain results clearly.
• Documentation & Best Practices: Maintain reproducible code, clean documentation, and version-controlled artifacts; contribute to engineering standards and peer reviews.

Required Skillset

• Experience: 3–5 years of hands-on machine learning experience with at least one model successfully deployed to production.
• Core ML: Strong grounding in supervised learning, regression, classification, ensemble methods, and time-series forecasting.
• Programming: Proficiency in Python and the scientific stack — pandas, NumPy, scikit-learn, PyTorch (or TensorFlow) — with production-grade coding practices.
• Feature Engineering: Solid understanding of feature design, scaling, encoding, handling missing data, and preventing data leakage.
• MLOps Fundamentals: Working knowledge of Docker, REST API serving (FastAPI / Flask), experiment tracking (MLflow / Weights & Biases), and at least one cloud ML platform (AWS, GCP, or Azure).
• Data Engineering Fluency: Comfortable with SQL, workflow orchestrators (Airflow / Prefect / Dagster), and modern data warehouses (Snowflake / BigQuery / Redshift).
• Version Control & CI/CD: Git, pull-request workflows, and exposure to continuous integration and deployment pipelines for ML projects.
• Statistics & Evaluation: Strong understanding of evaluation metrics, statistical significance, cross-validation, and bias / variance trade-offs.
• Communication: Ability to explain modeling choices, accuracy trade-offs, and probabilistic outputs to both technical and non-technical audiences.

Additional / Nice-to-Have Skillsets

• Advanced time-series and deep learning models (TFT, DeepAR, N-BEATS, LSTM, Transformers).
• Hyperparameter optimisation (Optuna, Ray Tune) and data quality tools (Great Expectations, Soda Core).
• Transformation tooling (dbt, PySpark); feature stores (Feast, Tecton); drift / observability (Evidently AI, WhyLabs, Arize).
• Operations research / optimisation exposure (OR-Tools, PuLP, Gurobi).
• BI and dashboarding skills: Power BI, Streamlit, or Tableau.
• Familiarity with LLMs, NLP (Hugging Face), or computer vision pipelines.