Senior Machine Learning Engineer

Senior Machine Learning Engineer – Job Description
Key Responsibilities
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.
Passion for experimentation, innovation, and continuous improvement