Machine Learning Engineer

Company Description

Nomad Labs is a forward-thinking technology team focused on unlocking the full potential of cutting-edge solutions to help businesses across various industries thrive. Specialising in custom software development, Nomad Labs combines advanced technologies like AI and data analytics with intuitive UX/UI designs to deliver tailored, performance-oriented solutions. The company adopts a human-centric approach, working closely with partners to understand unique challenges and deliver impactful results. At Nomad Labs, innovation and collaboration are at the forefront, enhancing business efficiency and driving lasting success.

Role Description

This is a full-time hybrid role for a Machine Learning Engineer, based in the Greater Kolkata Area with flexibility for remote work. The Machine Learning Engineer will design, develop, and deploy machine learning models to address complex challenges. Their responsibilities will also include data preprocessing, algorithm optimization, and leveraging artificial intelligence techniques for business-driven insights. Collaboration with cross-functional teams to align technological capabilities with organisational goals is a key part of this role.

Qualifications

• Translate model prototypes from Data Scientists into Azure ML production pipelines, including data ingestion, training, inference, and retraining.
• Build and iterate on ML models (forecasting/classification/regression) using modern ML frameworks(scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow).
• Develop robust feature pipelines (deterministic code, modular definitions, reusability) using Pandas/PySpark and orchestrate in AML Pipelines Jobs
• Design experiments with proper sampling, train-test splits, cross-validation, and metrics selection (e.g., RMSE, AUC, MAPE).
• Implement model selection, champion/challenger promotion, and versioning strategies.
• Document experiment results for reproducibility and regulatory compliance.
• Production ready models as batch or real-time endpoints via Azure ML.
• Implement model validation gates (drift/shift, prediction distribution checks, champion vs. challenger results).
• Set up model monitoring dashboards for latency, prediction freshness, data drift, and feature importance tracking.
• Integrate model deployment/test harnesses with Azure DevOps pipelines for CI/CD.
• Develop FastAPIs to invoke and consume ML models.
• Profile, clean, and transform raw data from Snowflake, SQL, and third-party sources.
• Implement checks for data quality (nulls, schema validation, outlier handling, time alignment, duplicate detection).
• Automate feature extraction and maintain feature store consistency.
• Work with Product, Data, and QA teams to agree on model acceptance criteria and experiment reviews.
• Contribute to defect taxonomy (data/model/serving), pipeline observability, and SLO dashboards.

Required Qualifications

• 5+ years developing data-focused solutions (3+ years in ML modeling and operations).

• Advanced proficiency in Python (pandas, NumPy, ML frameworks), SQL, and cloud data tools.

• Solid experience building production ML pipelines (Azure ML, Databricks, or equivalent).

• Understanding of model validation, drift detection, and online monitoring.

• Experience with feature stores, CI/CD (Azure DevOps), and API development (FastAPI/Flask).

• Bachelor’s/Master’s degree in Computer Science, Statistics, Information Technology or related field.

• Certification in Azure Data or ML Engineer Associate is a plus.