Senior ML(Machine Learning) Engineer

Machine Learning Development

• Design, build, and deploy robust ML models using Python and industry-standard ML frameworks (TensorFlow, PyTorch, Scikit‑learn, XGBoost, etc.).
• Collaborate with data scientists to translate prototypes into production-ready systems.
• Perform feature engineering, data preprocessing, model selection, hyperparameter tuning, and performance optimization.

MLOps & Productionalization

• Develop and maintain ML pipelines using AWS SageMaker, MLflow, H2O.ai, and other automation tools.
• Implement best practices for model versioning, lineage tracking, model performance monitoring, and retraining.
• Set up CI/CD pipelines for ML services and automate deployment workflows.

Cloud & Distributed Systems

• Architect and operate scalable ML workflows in AWS, including SageMaker, Step Functions, S3, ECR, CloudWatch, IAM, etc.
• Build and optimize distributed data processing pipelines using PySpark and AWS EMR.
• Ensure reliability, scalability, and cost efficiency of ML environments.

Data Engineering Integration

• Work closely with data engineering teams to build robust data ingestion and transformation pipelines.
• Improve data quality, reliability, and observability for ML use cases.
• Heavy hands-on coding with PySpark, SQL, and Python-based ETL workflows.

Collaboration & Leadership

• Provide technical mentorship and guidance to junior ML engineers and data scientists.
• Lead architectural discussions and participate in design reviews.
• Partner with cross-functional teams to scope and deliver ML-driven products.

Required Qualifications

• 5–8+ years of professional experience in ML engineering, data engineering, or related fields.
• Expert-level proficiency in Python and ML frameworks (Scikit‑learn, TensorFlow, PyTorch, H2O, XGBoost, etc.).
• Hands-on experience with AWS SageMaker for training, tuning, deployment, and pipeline automation.
• Strong knowledge of H2O.ai (Driverless AI or H2O3), AutoML frameworks, and enterprise ML workflows.
• Proficient with MLflow for experiment tracking, model packaging, and deployment.
• Advanced experience with PySpark and distributed data processing.
• Experience with AWS EMR for Spark cluster management and large‑scale data transformations.
• Solid understanding of MLOps concepts: CI/CD for ML, feature stores, monitoring, drift detection, model governance.
• Strong background in object‑oriented programming, algorithm design, and software engineering best practices.
• Experience with Docker and containerized ML workloads.

Preferred Qualifications

• Knowledge of Kubernetes (EKS) for ML deployment.
• Experience implementing model monitoring systems (e.g., Neptune, SageMaker Model Monitor, custom solutions).
• Familiarity with microservices, REST APIs, and event-driven architectures.
• Experience with large language models (LLMs) and vector databases is a plus.

Soft Skills

• Excellent problem-solving and analytical skills.
• Strong communication and documentation abilities.
• Ability to operate in a fast-paced, cross-functional environment.