Sr. Machine Learning Engineer

? Location: Bengaluru, Karnataka, India

? Industry: Personal care product manufacturing

? Work Setting: Hybrid

Are you passionate about building scalable machine learning platforms, automating AI deployments, and bridging the gap between data science and production engineering?

An experienced Senior MLOps Engineer to design, implement, and optimize enterprise-grade machine learning infrastructure and deployment frameworks. In this role, you will be responsible for building scalable ML pipelines, automating model lifecycle management, and ensuring AI solutions are secure, reliable, cost-efficient, and production-ready. You will collaborate closely with Data Scientists, DevOps teams, and technology stakeholders to deliver robust AI/ML platforms that accelerate innovation and business value.

Key Responsibilities

Machine Learning Platform Engineering

- Design, develop, and maintain scalable machine learning platforms and infrastructure that support model development, training, deployment, and monitoring.
- Build reusable frameworks and deployment templates that standardize machine learning operations across multiple use cases.
- Establish best practices for model lifecycle management, automation, governance, and operational excellence.
- Drive the development of scalable, secure, and production-ready MLOps capabilities.

ML Pipeline Development & Automation

- Design and manage end-to-end machine learning pipelines for data ingestion, model training, validation, deployment, and monitoring.
- Automate machine learning workflows to improve efficiency, reproducibility, and reliability.
- Optimize large-scale data processing and machine learning training workloads.
- Implement frameworks that support rapid deployment and continuous improvement of AI solutions.

DevOps, CI/CD & Security Integration

- Develop and maintain CI/CD pipelines for machine learning applications and services.
- Integrate code quality, security validation, automated testing, and governance controls into deployment workflows.
- Implement DevSecOps practices to ensure secure and compliant delivery of machine learning solutions.
- Promote infrastructure automation and continuous deployment best practices.

Model Deployment & Orchestration





- Containerize and deploy machine learning applications using contemporary orchestration technologies.
- Design scalable deployment architectures that support high availability and performance requirements.
- Manage APIs and integration services that connect machine learning models with enterprise applications.
- Ensure smooth transition of machine learning models from development to production environments.

Model Monitoring & Lifecycle Management

- Monitor model performance, data quality, and operational health across environments.
- Implement solutions for data drift detection, model retraining, and performance optimization.
- Establish automated validation and monitoring processes to maintain model accuracy and reliability.
- Support governance and auditability of machine learning assets and workflows.

Cloud Engineering & Cost Optimization

- Design and optimize cloud-native machine learning architectures.
- Monitor resource utilization and implement cost-management strategies for compute-intensive workloads.
- Ensure AI infrastructure remains scalable, resilient, secure, and cost-effective.
- Support platform modernization and cloud optimization initiatives.

Documentation & Technical Leadership

- Create and maintain technical documentation, deployment guides, architecture diagrams, and operational runbooks.
- Define standards, templates, and best practices for machine learning operations.
- Provide technical mentorship and guidance to engineering and data science teams.
- Support knowledge sharing and continuous improvement initiatives across the organization.

Cross-Functional Collaboration

- Act as a key liaison between Data Science, Engineering, DevOps, Architecture, and IT teams.
- Collaborate with stakeholders to understand business requirements and translate them into scalable ML solutions.
- Support deployment planning, release management,



and production support activities.
- Drive alignment across development, testing, and production environments.

Required Qualifications

Education

- Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related technical field.

Experience

- 5+ years of experience in MLOps, Machine Learning Engineering, DevOps, Cloud Engineering, or related fields.
- Proven experience deploying and supporting machine learning models in production environments.
- Experience designing scalable AI/ML infrastructure in enterprise environments.
- Strong background in automation, platform engineering, and cloud-based machine learning solutions.

Technical Skills Machine Learning & MLOps

- Machine Learning Lifecycle Management
- MLOps Frameworks and Best Practices
- Model Deployment and Monitoring
- Model Governance and Versioning
- Data Drift Detection and Model Optimization
- Experiment Tracking and Reproducibility

Cloud & Data Platforms

- Microsoft Azure
- Azure Machine Learning
- Databricks
- Distributed Data Processing
- PySpark
- Cloud-Native Architecture

Containerization & Orchestration

- Docker
- Kubernetes
- Azure Kubernetes Service (AKS)
- Container Orchestration
- Scalable Deployment Architectures

DevOps & Automation

- CI/CD Pipeline Design
- GitHub Actions
- DevSecOps Practices
- Infrastructure Automation
- Release Management
- Code Quality Automation

API & Platform Integration

- REST APIs
- Microservices Architecture
- Service Integration
- API Security and Management

Monitoring & Optimization

- Performance Monitoring
- Cost Optimization
- Resource Utilization Analysis
- Automated Alerting and Diagnostics
- Reliability Engineering

Preferred Qualifications

- Experience with enterprise solution architecture and system design principles.
- Azure certifications such as:
- AI-900
- DP-100
- AZ-305
- Other cloud and AI-related certifications
- Experience supporting large-scale machine learning initiatives in enterprise environments.
- Familiarity with advanced governance, compliance, and security frameworks for AI platforms.
- Experience creating reusable machine learning platform components and accelerators.

📌 Sr. Machine Learning Engineer (Bengaluru)
🏢 SoTalent
📍 Bengaluru