Machine Learning Engineer

Bengaluru
January 23, 2026
Full Time
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Job Description

Role Overview

We are looking for a hands-on Machine Learning Engineer (5+ years experience) who can design, develop, deploy, and optimize machine learning models and pipelines in a cloud-native AWS environment.

The ideal candidate has strong programming fundamentals, deep experience with Python and ML frameworks, and solid exposure to MLOps, containerization, and production-grade ML systems. You will work independently within an agile product engineering team, focusing on scalability, performance, reliability, and maintainability of ML solutions.

Key Responsibilities

Machine Learning Development

  • Design, develop, train, and optimize machine learning models for production use.
  • Perform feature engineering, hyperparameter tuning, and model evaluation.
  • Monitor and evaluate model performance using validation, drift, and accuracy metrics.
  • Handle data drift and model drift using metrics such as PSI, KS test, F1 Score, ROC-AUC, and RMSE.

MLOps & Model Deployment

  • Build and deploy ML models as containerized APIs/services using:
    • AWS SageMaker
    • FastAPI / Flask (or similar frameworks)
  • Manage the full ML lifecycle: training, validation, deployment, monitoring.
  • Implement CI/CD pipelines for ML workflows (GitLab CI, MLflow integration).
  • Maintain model and dataset versioning.
  • Implement observability (logging, metrics, monitoring) for ML services.
  • Work with Docker and orchestration tools for scalable deployments.
  • Good to have: Experience with Databricks for large-scale ML workflows.

Databases & Data Storage

  • Work with relational databases such as PostgreSQL or MySQL.
  • Write optimized SQL queries with proper joins, indexing, and transactions.
  • Experience working with Vector Databases.
  • Exposure to Redis or NoSQL databases is a plus.

API Development & Integration

  • Design and develop RESTful APIs for ML model serving.
  • Document APIs using Swagger / OpenAPI.
  • Implement request/response validation, API versioning, and error handling.
  • Integrate ML services with production systems.

Engineering Best Practices (Mandatory)

  • Write clean, maintainable, and well-structured code.
  • Follow Git-based workflows and participate in code reviews.
  • Implement proper logging, exception handling, and unit tests.
  • Apply software engineering fundamentals (modular design, reusable components).

Performance, Reliability & Security

Performance

  • Optimize database queries and API performance.
  • Understand caching, pagination, and async processing basics.

Reliability

  • Implement retry mechanisms, timeouts, and fallback strategies.
  • Ensure resilience of production ML systems.

Security

  • Implement API security practices (OAuth2, authentication, authorization).
  • Enforce rate limiting and input validation.
  • Follow secure coding practices to prevent SQL injection and vulnerabilities.
  • Ensure compliance with data privacy standards (GDPR, encryption).

Collaboration & Soft Skills

  • Strong English communication skills.
  • Ability to explain technical decisions clearly to stakeholders.
  • Comfortable working in Agile (Scrum/Kanban) teams.
  • Participate actively in stand-ups, sprint planning, reviews, and retrospectives.
  • Proactive, dependable, and able to work independently once requirements are defined.
  • Comfortable collaborating across time zones.

Expected Deliverables

  • Production-ready ML models deployed via APIs and containers.
  • CI/CD pipelines for ML training and deployment.
  • High-quality, standards-compliant code.
  • Bug fixes, enhancements, and production support.
  • Accurate sprint updates and timely delivery.
  • Clear technical documentation for APIs and ML services.

Experience & Qualifications

Experience

  • 5+ years overall experience as a:
    • Machine Learning Engineer
    • MLOps Engineer

Education

  • UG: Any Graduate
  • PG: Any Postgraduate

Key Skills

Python, Machine Learning, TensorFlow, PyTorch, scikit-learn, Pandas, NumPy, SQL, PostgreSQL, Docker, AWS, SageMaker, MLflow, Git, CI/CD, REST APIs, FastAPI, Flask, Secure Coding, Model Deployment, Data Drift, MLOps, Vector Databases

Good-to-Have Certifications

  • AWS Certified Machine Learning – Specialty (Optional)

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