๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ญ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฎ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ญ๐ฌ-๐ฎ๐ฌ ๐๐ฃ๐)
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.