ML Engineer II - Aws, Aws Cloud at Global Digital Transformation Solutions Provider · Pune · 6 - 12 years · ₹10L - ₹30L / yr · Posted 6 Nov 2025
ML Engineer II - Aws, Aws Cloud
at Global Digital Transformation Solutions Provider
Amazon Web Services (AWS)
Elastic Search
Docker
Kubernetes
Machine Learning (ML)
Deep LearningJob Details
- Job Title: ML Engineer II - Aws, Aws Cloud
- Industry: Technology
- Domain - Information technology (IT)
- Experience Required: 6-12 years
- Employment Type: Full Time
- Job Location: Pune
- CTC Range: Best in Industry
Job Description:
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, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (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 Sagemaker 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)
Skills: Aws, Aws Cloud, Amazon Redshift, Eks
Must-Haves
Aws, Aws Cloud, Amazon Redshift, Eks
NP: Immediate – 30 Days
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