Data & AI - ML Engineer

Job Description:

Job Description – Lead Machine Learning Engineer / ML Architect
Role Overview
We are seeking a highly skilled Lead Machine Learning Engineer / Architect with expertise in end-to-end ML pipeline design, model training, optimization, and large-scale deployment. The ideal candidate will bring 10+ years of experience, strong knowledge of ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, CatBoost), and proven expertise in MLOps tools (MLflow, Kubeflow, Azure ML Pipelines). This role requires a balance of hands-on model engineering and technical leadership to deliver production-ready ML systems.
Key Responsibilities
Machine Learning & Model Development
Build, train, and optimize models using scikit-learn, PyTorch, TensorFlow, Keras, XGBoost, LightGBM, and CatBoost.
Develop and test features using pandas, NumPy, and PySpark for large-scale datasets.
Apply advanced techniques for hyperparameter tuning, model interpretability, and ensemble methods.
Model Deployment & Serving
Deploy models to production using TensorFlow Serving, TorchServe, and ONNX/TensorRT for optimized inference.
Design scalable APIs and endpoints for real-time and batch prediction services.
Implement containerized deployments (Docker, Kubernetes) for portability and scalability.
MLOps & Automation
Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines.
Implement model versioning, tracking, and experiment management.
Establish CI/CD workflows for ML models, ensuring reproducibility and continuous improvement.
Optimization & Performance
Optimize models for latency, throughput, and cost efficiency in production environments.
Use ONNX/TensorRT for inference acceleration and cross-platform model deployment.
Monitor deployed models for drift, bias, and performance degradation.
Leadership & Collaboration
Lead and mentor ML engineers and data scientists on best practices in ML and MLOps.
Collaborate with data engineering teams to ensure seamless integration with pipelines.
Partner with product managers and business stakeholders to design scalable AI-driven solutions.
Required Skills & Qualifications
10+ years of IT experience with hands-on ML/AI engineering.
Expertise in:
Core ML frameworks: pandas, NumPy, scikit-learn, PySpark
Advanced algorithms/libraries: XGBoost, LightGBM, CatBoost
Deep learning: TensorFlow, Keras, PyTorch
Model optimization & serving: ONNX, TensorRT, TensorFlow Serving, TorchServe
Strong knowledge of MLOps tools: MLflow, Kubeflow, Azure ML Pipelines.
Hands-on with cloud environments (Azure, AWS, GCP) and container orchestration (Kubernetes).
Proven experience in leading ML projects, mentoring teams, and deploying production AI systems.
Preferred Skills
Knowledge of distributed training strategies (Horovod, DDP).
Familiarity with feature stores (Feast, Tecton, Databricks Feature Store).
Experience with responsible AI frameworks (explainability, fairness, bias mitigation).
Exposure to multi-model orchestration and hybrid cloud ML architectures.

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