Machine Learning SME

' : - ( - ) 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.