Sr. AI/ML Engineer

PERMEVO; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

PERMEVO is seeking an experienced Senior AI/ML Engineer to design, develop, and deploy scalable ML solutions powering intelligent products and data-driven decisions.

You will build production-grade ML systems, automated training and deployment pipelines, and integrate AI into large-scale applications, collaborating with data scientists, software engineers, and platform teams to ensure reliability and scalability.

Qualifications

• Bachelor's or Master's degree in Computer Science, ML, Data Science, Engineering, or a related field.
• 8+ years of software engineering experience.
• 3+ years designing and deploying machine learning models in production.
• Strong programming proficiency in Python.
• Experience with ML frameworks and libraries: Scikit-learn, XGBoost, TensorFlow, LightGBM.
• Hands-on experience deploying and managing ML workloads using SageMaker or equivalent.
• Strong understanding of cloud-native architectures and large-scale data systems.
• Excellent analytical, debugging, and problem-solving skills; ability to collaborate cross-functionally.

Responsibilities

• Design, develop, and deploy scalable ML models for production use cases.
• Build production-grade ML workflows and pipelines.
• Automate model training, deployment, and lifecycle management.
• Integrate ML capabilities into backend services and APIs.
• Monitor performance, detect drift, and retrain as needed.
• Collaborate with data scientists, software engineers, and platform teams.
• Mentor engineers and share best practices across ML initiatives.
• Drive AI innovation at scale.

Skills

Python programming

Cloud architectures

MLOps

Mentoring

Problem solving

Cross-functional collaboration

Education

Bachelor's or Master's in Computer Science / ML / Data Science / Engineering

Tools

Amazon SageMaker

Scikit-learn

XGBoost

TensorFlow

LightGBM

Job description

We are seeking an experienced Senior AI/ML Engineer to design, develop, and deploy scalable machine learning solutions that power intelligent products and data-driven decision-making.

In this role, you will be responsible for building production-grade ML systems, developing automated model training and deployment pipelines, and integrating machine learning capabilities into large-scale applications. You will collaborate closely with data scientists, software engineers, and platform teams to deliver reliable, scalable, and maintainable AI solutions.

This position is ideal for engineers who enjoy solving complex problems, working with cloud-native machine learning platforms, and driving AI innovation at scale.

• Design, develop, and deploy machine learning models for predictive analytics, optimization, and intelligent automation use cases.
• Build scalable and production-ready ML workflows using Amazon SageMaker.
• Develop end-to-end machine learning pipelines, including:
  • Data preparation
• Automate model lifecycle management using Amazon SageMaker Pipelines and MLOps best practices.
• Integrate machine learning models into backend services, APIs, and distributed systems.
• Monitor model performance, detect model drift, and implement retraining strategies to maintain accuracy and reliability.
• Collaborate with software engineering and platform teams to ensure ML systems are scalable, secure, and operationally efficient.
• Improve deployment, monitoring, and observability of machine learning infrastructure.
• Participate in architecture discussions and contribute to AI/ML platform design decisions.
• Mentor engineers and share best practices across machine learning engineering initiatives.

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field.

• 8+ years of overall software engineering experience.
• 3+ years of hands-on experience designing and deploying machine learning models in production environments.
• Strong programming proficiency in Python.
• Experience with machine learning frameworks and libraries, including:
  • Scikit-learn
  • XGBoost
  • TensorFlow
  • LightGBM
• Hands-on experience deploying and managing machine learning workloads using Amazon SageMaker or equivalent cloud-based ML platforms.
• Strong understanding of:
  • Experience working with cloud-native architectures and large-scale data systems.
  • Strong analytical, debugging, and problem-solving skills.
  • Ability to collaborate effectively with cross-functional engineering and product teams.
• Experience building production-grade ML pipelines and automated model training workflows.
• Exposure to MLOps practices and ML lifecycle management tools.
• Experience with distributed data processing platforms and large-scale data ecosystems.
• Knowledge of advanced AI techniques, including:
  • Experience implementing monitoring, observability, and governance frameworks for machine learning systems.
  • Familiarity with cloud-based data engineering and modern deployment architectures.
• Experience mentoring engineers and leading technical initiatives.