Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Full-time — employment source
Location: pune, Hyderabad, Telangana, India, Bengaluru, Karnataka, India Job Type: Full-time We are looking for an experienced MLOps / Machine Learning SME to lead the design, development, deployment, and operationalisation of machine learning solutions across the complete ML lifecycle. The role requires strong hands-on expertise in MLOps, Machine Learning, Python, AWS SageMaker, and AWS Bedrock, along with the ability to provide technical leadership and work directly with clients and cross-functional stakeholders.
Read the full posting

What you’ll work on

Design and implement end-to-end MLOps pipelines covering model development, training, deployment, monitoring, and lifecycle management.
Develop and productionise machine learning solutions using Python and modern ML frameworks.
Build scalable ML workflows and infrastructure using AWS SageMaker.

From the employer’s posting

Key Responsibilities Design and implement end-to-end MLOps pipelines covering model development, training, deployment, monitoring, and lifecycle management. Develop and productionise machine learning solutions using Python and modern ML frameworks.

Design and implement end-to-end MLOps pipelines covering model development, training, deployment, monitoring, and lifecycle management. Develop and productionise machine learning solutions using Python and modern ML frameworks. Build scalable ML workflows and infrastructure using AWS SageMaker.

Develop and productionise machine learning solutions using Python and modern ML frameworks. Build scalable ML workflows and infrastructure using AWS SageMaker. Leverage AWS Bedrock to develop, integrate, and operationalise AI and foundation-model-based solutions.

Tools in this posting

Python
AWS
SageMaker

Source

Job Type: Full-time We are looking for an experienced MLOps / Machine Learning SME to lead the design, development, deployment, and operationalisation of machine learning solutions across the complete ML lifecycle. The role requires strong hands-on expertise in MLOps, Machine Learning, Python, AWS SageMaker, and AWS Bedrock, along with the ability to provide technical leadership and work directly with clients and cross-functional stakeholders. The ideal candidate will combine deep technical expertise with strong problem-solving and communication skills to build reliable, scalable, and production-ready machine learning platforms and solutions.

- Design and implement end-to-end MLOps pipelines covering model development, training, deployment, monitoring, and lifecycle management. - Develop and productionise machine learning solutions using Python and modern ML frameworks. - Build scalable ML workflows and infrastructure using AWS SageMaker.

- Strong hands-on expertise in end-to-end MLOps and Machine Learning. - Advanced proficiency in Python for machine learning and production engineering. - Mandatory hands-on experience with AWS SageMaker.

- Develop and productionise machine learning solutions using Python and modern ML frameworks. - Build scalable ML workflows and infrastructure using AWS SageMaker. - Leverage AWS Bedrock to develop, integrate, and operationalise AI and foundation-model-based solutions.

- Build scalable ML workflows and infrastructure using AWS SageMaker. - Leverage AWS Bedrock to develop, integrate, and operationalise AI and foundation-model-based solutions. - Deploy machine learning models into scalable and reliable production environments.

- Advanced proficiency in Python for machine learning and production engineering. - Mandatory hands-on experience with AWS SageMaker. - Mandatory experience with AWS Bedrock and foundation-model/GenAI solutions.

- Mandatory hands-on experience with AWS SageMaker. - Mandatory experience with AWS Bedrock and foundation-model/GenAI solutions. - Strong understanding of ML model development, deployment, monitoring, and lifecycle management.

Job description

View original posting ↗

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟮𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟬-𝟮𝟬 𝗟𝗣𝗔)

Experience: 6+ yrs

Location: pune, Hyderabad, Telangana, India, Bengaluru, Karnataka, India

Job Type: Full-time

We are looking for an experienced MLOps / Machine Learning SME to lead the design, development, deployment, and operationalisation of machine learning solutions across the complete ML lifecycle. The role requires strong hands-on expertise in MLOps, Machine Learning, Python, AWS SageMaker, and AWS Bedrock, along with the ability to provide technical leadership and work directly with clients and cross-functional stakeholders.

The ideal candidate will combine deep technical expertise with strong problem-solving and communication skills to build reliable, scalable, and production-ready machine learning platforms and solutions.

Requirements

Key Responsibilities

Design and implement end-to-end MLOps pipelines covering model development, training, deployment, monitoring, and lifecycle management.
Develop and productionise machine learning solutions using Python and modern ML frameworks.
Build scalable ML workflows and infrastructure using AWS SageMaker.
Leverage AWS Bedrock to develop, integrate, and operationalise AI and foundation-model-based solutions.
Deploy machine learning models into scalable and reliable production environments.
Implement model monitoring, performance tracking, drift detection, alerting, and continuous improvement processes.
Develop automated workflows for model training, validation, deployment, and retraining.
Establish best practices for ML experimentation, versioning, reproducibility, governance, and deployment.
Collaborate with Data Scientists, Data Engineers, Software Engineers, DevOps, Cloud Architects, and Product teams.
Troubleshoot complex issues across ML pipelines, infrastructure, deployments, and production environments.
Optimise ML workloads for performance, scalability, reliability, and cost efficiency.
Evaluate emerging machine learning and AI technologies and identify opportunities for practical adoption.
Provide technical guidance and mentorship to engineering and machine learning teams.
Lead technical discussions, solution reviews, architecture sessions, and client-facing engagements.
Translate business and client requirements into scalable ML and MLOps solutions.
Prepare technical documentation, architecture designs, implementation approaches, and operational guidelines.
Contribute to engineering standards, reusable frameworks, automation, and continuous improvement initiatives.

What Makes You a Great Fit

6+ years of experience in Machine Learning, MLOps, ML Engineering, AI Engineering, or a closely related technical field.
Strong hands-on expertise in end-to-end MLOps and Machine Learning.
Advanced proficiency in Python for machine learning and production engineering.
Mandatory hands-on experience with AWS SageMaker.
Mandatory experience with AWS Bedrock and foundation-model/GenAI solutions.
Strong understanding of ML model development, deployment, monitoring, and lifecycle management.
Experience building production-grade ML pipelines and automated model deployment workflows.
Strong understanding of cloud infrastructure, CI/CD, containers, APIs, and scalable application architectures.
Experience with model monitoring, observability, model performance, drift, and reliability practices.
Strong troubleshooting and problem-solving skills across machine learning and cloud environments.
Proven experience working as a Technical SME, Lead, or senior technical contributor.
Strong client-facing experience with excellent communication and presentation skills.
Ability to explain complex ML and MLOps concepts to both technical and non-technical stakeholders.
Strong stakeholder management and cross-functional collaboration skills.
Ability to work independently, take ownership of complex technical initiatives, and provide effective technical leadership.
Experience working in Agile environments and managing multiple priorities effectively.
A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline is preferred.