Job Title: Machine Learning Engineer / MLOps Engineer
Experience: 5+ Years
Location: Hyderabad
Employment Type: Full-Time
About the Role
We are seeking a highly motivated and experienced Machine Learning Engineer / MLOps Engineer to join our growing AI and Data Science team. The ideal candidate will have strong expertise in building, deploying, and managing machine learning solutions at scale using AWS SageMaker, MLflow, H2O.ai, PySpark, and MLOps best practices.
This role involves working closely with Data Scientists, Data Engineers, and Cloud Engineering teams to operationalize machine learning models, build robust ML pipelines, and ensure reliable model deployment, monitoring, and governance in production environments.
Key Responsibilities
Machine Learning Model Development & Deployment
- Collaborate with Data Scientists to develop, train, validate, and deploy machine learning models.
- Build scalable ML solutions using AWS SageMaker and cloud-native services.
- Implement model deployment strategies for real-time and batch inference workloads.
- Support the full machine learning lifecycle from experimentation to production.
MLOps & Model Lifecycle Management
- Design and implement end-to-end MLOps pipelines for model training, validation, deployment, monitoring, and retraining.
- Establish CI/CD processes for machine learning workflows.
- Automate model versioning, deployment, rollback, and governance processes.
- Implement model monitoring, drift detection, and performance tracking mechanisms.
AWS SageMaker & Feature Engineering
- Develop and manage machine learning workflows using AWS SageMaker.
- Build and maintain feature engineering pipelines using SageMaker Feature Store.
- Optimize feature storage, retrieval, and reuse across machine learning projects.
- Leverage SageMaker capabilities for experimentation, training, deployment, and monitoring.
ML Experiment Tracking & Governance
- Utilize MLflow for experiment tracking, model registry, model versioning, and lifecycle management.
- Maintain reproducibility and traceability of machine learning experiments.
- Implement governance and compliance controls for ML assets.
Big Data Processing & Analytics
- Process and transform large-scale datasets using PySpark.
- Design efficient data preparation and feature engineering workflows.
- Work with structured and unstructured data sources to support machine learning initiatives.
Advanced Analytics & AutoML
- Leverage H2O.ai frameworks for model development, evaluation, and AutoML capabilities.
- Compare and optimize machine learning algorithms for business use cases.
- Support predictive modeling, classification, regression, clustering, and forecasting solutions.
Cloud & Platform Engineering
- Work with AWS cloud services to build scalable ML infrastructure.
- Ensure reliability, scalability, security, and cost optimization of ML workloads.
- Collaborate with DevOps and Cloud Engineering teams on infrastructure automation.
Required Skills & Qualifications
Experience
- 5+ years of experience in Machine Learning Engineering, MLOps, Data Science Engineering, or related roles.
- Proven experience operationalizing machine learning models in production environments.
Technical Skills
- Strong expertise in AWS SageMaker.
- Hands-on experience with SageMaker Feature Store.
- Experience implementing and managing MLOps frameworks and workflows.
- Strong proficiency in Python for machine learning and data engineering.
- Experience with MLflow for experiment tracking and model lifecycle management.
- Hands-on experience with PySpark for distributed data processing.
- Experience with H2O.ai and AutoML frameworks.
- Strong understanding of machine learning algorithms and model evaluation techniques.
Data & Analytics
- Experience building feature engineering pipelines.
- Strong SQL and data manipulation skills.
- Knowledge of data preprocessing, feature selection, and model optimization techniques.
Preferred Skills
- Experience with AWS services such as:
- S3
- Lambda
- Glue
- EMR
- ECS/EKS
- CloudWatch
- IAM
- Experience with Docker and Kubernetes.
- Knowledge of model monitoring and observability frameworks.
- Familiarity with DataOps and DevOps practices.
- Exposure to Generative AI, LLMs, and AI platform engineering is a plus.
Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Ability to work in cross-functional teams and Agile environments.
- Strong ownership mindset with attention to scalability and operational excellence.
