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Open nowPosted 7 hours ago

Machine Learning SME

Workable (global search)107,765 open roles

Where
Bengaluru, KA, India
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Your applicationOpen nowMachine Learning SMEWorkable (global search) · Bengaluru, KA, India

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Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 2 days.

Share of postings closed within
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  2. 3.6%3 days
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  4. 14.9%14 days
  5. 34.2%30 days
This job: posted 7 hours ago

Workable (global search) median: 2 days open

The 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.
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