AWS SageMaker Engineer

Persistent Systems; Pune District; Hybrid; INR 1,500,000 - 2,500,000; Full time

Benefits offered by this job

Competitive salary and benefits package

Culture focused on talent development

Insurance coverage for family members

Employee engagement initiatives like project parties

Flexible work hours

Job summary

Persistent Systems is seeking an experienced AWS SageMaker Engineer to design scalable MLOps platforms and manage the ML lifecycle. The candidate must demonstrate strong expertise in SageMaker, cloud-native architecture, and DevOps practices, collaborating with data science teams to efficiently implement ML use cases.

With 8-12 years of experience, responsibilities include building ML pipelines, CI/CD frameworks, and optimizing SageMaker workloads for performance. This role supports a hybrid work environment and promotes a culture of inclusion and talent development.

Qualifications

• 8-12 years of hands-on experience in DevOps, MLOps, or ML platform engineering.
• Strong expertise with Amazon SageMaker for building and operating ML pipelines.
• Advanced proficiency in Python for automation and pipeline development.
• Deep experience with AWS services supporting ML platforms.
• Proven experience building enterprise-scale DevOps and MLOps automation frameworks.

Responsibilities

• Design and implement end-to-end MLOps pipelines on Amazon SageMaker.
• Build DevOps automation for ML infrastructure management.
• Collaborate with data science and engineering teams to productionize ML use cases.
• Drive platform best practices and documentation.

Skills

AWS services

Amazon SageMaker

DevOps practices

Python

CI/CD

MLOps

Cloud-native architecture

Model monitoring

Problem-solving skills

Tools

S3

IAM

CloudWatch

Event Bridge

ECR

Job description

We are seeking an experienced AWS SageMaker Engineer to design and build scalable MLOps platforms and machine learning pipelines on AWS. The role focuses on enabling end-to-end ML lifecycle management including model development, deployment, monitoring, and automation. The ideal candidate will have strong expertise in SageMaker, cloud-native architecture, and DevOps practices, along with the ability to collaborate with data science teams to productionize ML use cases efficiently.

• Location: All Persistent Location
• Experience: 8-12 years
• Job Type: Full Time Employment

What You'll Do:

• Design and implement end-to-end MLOps pipelines on Amazon SageMaker for training, validation, deployment, and monitoring
• Build DevOps automation to provision and manage ML infrastructure across environments
• Develop Python-based tools and frameworks to standardize ML workflows and platform operations
• Implement CI/CD pipelines for ML models, feature pipelines, and SageMaker jobs
• Automate model packaging, versioning, promotion, and rollback across Dev/Test/Prod
• Enforce security, governance, and compliance controls for ML platforms and data access
• Optimize SageMaker workloads for performance, scalability, and cost efficiency
• Integrate SageMaker with AWS services such as S3, IAM, CloudWatch, Event Bridge, and ECR
• Implement monitoring and alerting for model performance, drift, failures, and infrastructure health
• Collaborate with data science and engineering teams to productionize ML use cases
• Troubleshoot complex platform, pipeline, and deployment issues in distributed ML systems
• Drive platform best practices, documentation, and continuous improvement initiatives

Expertise You'll Bring:

• 8-12 years of hands‑on experience in DevOps, MLOps, or ML platform engineering
• Strong expertise with Amazon SageMaker for building and operating ML pipelines
• Advanced proficiency in Python for automation, pipeline development, and tooling
• Deep experience with AWS services supporting ML platforms and CI/CD
• Proven experience building enterprise‑scale DevOps and MLOps automation frameworks
• Strong understanding of CI/CD, Infrastructure as Code, and cloud‑native architectures
• Experience securing ML platforms using IAM, encryption, and network isolation
• Hands‑on exposure to model monitoring, drift detection, and lifecycle management
• Experience optimizing cloud resources for cost, scalability, and reliability
• Strong analytical and problem‑solving skills in distributed environments
• Ability to collaborate across data science, engineering, security, and operations teams
• Ownership mindset with the ability to drive platforms end‑to‑end
• Competitive salary and benefits package
• Culture focused on talent development with quarterly growth opportunities and company‑sponsored higher education and certifications
• Opportunity to work with cutting‑edge technologies
• Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards
• Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents

Values‑Driven, People‑Centric & Inclusive Work Environment:

Persistent is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds.

• We support hybrid work and flexible hours to fit diverse lifestyles.
• Our office is accessibility‑friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities.
• If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment

Let’s unleash your full potential at Persistent - persistent.com/careers

“Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind.”