ML Engineer II - Aws, Aws Cloud at Global Digital Transformation Solutions Provider · Pune · 6 - 12 years · ₹25L - ₹30L / yr · Posted 20 Nov 2025
ML Engineer II - Aws, Aws Cloud
at Global Digital Transformation Solutions Provider
Machine Learning (ML)
Amazon Web Services (AWS)
Kubernetes
Deep Learning
DockerMUST-HAVES:
- Machine Learning + Aws + (EKS OR ECS OR Kubernetes) + (Redshift AND Glue) + Sage maker
- Notice period - 0 to 15 days only
- Hybrid work mode- 3 days office, 2 days at home
SKILLS: AWS, AWS CLOUD, AMAZON REDSHIFT, EKS
ADDITIONAL GUIDELINES:
- Interview process: - 2 Technical round + 1 Client round
- 3 days in office, Hybrid model.
CORE RESPONSIBILITIES:
- The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
- Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
- Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
- Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
- System Integration: Integrate models into existing systems and workflows.
- Model Deployment: Deploy models to production environments and monitor performance.
- Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
- Continuous Improvement: Identify areas for improvement in model performance and systems.
SKILLS:
- Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
- Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaos search logs, etc. for troubleshooting; Other tech touch points are Scylla DB (like BigTable), OpenSearch, Neo4J graph
- Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
- Knowledge of model monitoring and performance evaluation.
REQUIRED EXPERIENCE:
- Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sage maker pipeline with ability to analyze gaps and recommend/implement improvements
- AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
- AWS data: Redshift, Glue
- Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)
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