AI Developer

Comviva; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Comviva Bengaluru is seeking a Machine Learning Engineer to design, develop, and deploy ML models using Python and frameworks such as TensorFlow or PyTorch, and to build AI-driven features integrated via APIs. The role includes end-to-end ML pipelines, API development, MCP-based orchestration, large-scale data processing, model production deployment, monitoring, and documentation of AI architectures.

The candidate will work on improving latency, scalability, and efficiency of AI workflows, while

Qualifications

• Strong Python programming and ML frameworks like TensorFlow or PyTorch.
• Experience deploying ML/AI models into production.
• Data preprocessing, feature engineering, and evaluating model performance.
• Develop APIs to integrate AI models with backend systems.
• Understanding MCP and AI system orchestration.

Responsibilities

• Design, develop, and deploy machine learning and AI models using Python and frameworks such as TensorFlow or PyTorch.
• Build AI‑driven features and integrate models into applications using APIs and microservices.
• Implement end‑to‑end ML pipelines including data preprocessing, training, validation, and evaluation.
• Develop APIs for seamless integration of AI services with enterprise applications.
• Implement Model Context Protocol (MCP) for efficient model communication and orchestration.
• Perform large‑scale data processing, feature engineering, and model training.
• Deploy models into production environments following MLOps best practices.
• Monitor model performance, manage versioning, and support retraining and lifecycle operations.
• Optimize model inference, data pipelines, and AI workflows for latency, scalability, and efficiency.
• Maintain detailed documentation for models, APIs, data flows, and AI system architecture.

Skills

Python

TensorFlow

PyTorch

ML pipelines

API development

MLOps

Model orchestration

Tools

Git

Docker

Kubernetes

AWS

Job description

• Design, develop, and deploy machine learning and AI models using Python and frameworks such as TensorFlow or PyTorch


• Build AI‑driven features and integrate models into applications using APIs and microservices


• Implement end‑to‑end ML pipelines including data preprocessing, training, validation, and evaluation


• Develop APIs for seamless integration of AI services with enterprise applications


• Implement Model Context Protocol (MCP) for efficient model communication and orchestration


• Perform large‑scale data processing, feature engineering, and model training


• Deploy models into production environments following MLOps best practices


• Monitor model performance, manage versioning, and support retraining and lifecycle operations


• Optimize model inference, data pipelines, and AI workflows for latency, scalability, and efficiency


• Maintain detailed documentation for models, APIs, data flows, and AI system architecture

Key Accountabilities

• Design, develop, and deploy machine learning and AI models using Python and frameworks such as TensorFlow or PyTorch


• Build AI‑driven features and integrate models into applications using APIs and microservices


• Implement end‑to‑end ML pipelines including data preprocessing, training, validation, and evaluation


• Develop APIs for seamless integration of AI services with enterprise applications


• Implement Model Context Protocol (MCP) for efficient model communication and orchestration


• Perform large‑scale data processing, feature engineering, and model training


• Deploy models into production environments following MLOps best practices


• Monitor model performance, manage versioning, and support retraining and lifecycle operations


• Optimize model inference, data pipelines, and AI workflows for latency, scalability, and efficiency


• Maintain detailed documentation for models, APIs, data flows, and AI system architecture

Mandatory Skills

• Strong proficiency in Python programming and ML frameworks such as TensorFlow or PyTorch


• Experience in building, evaluating, and deploying ML/AI models into production


• Strong understanding of data preprocessing, feature engineering, and model performance evaluation


• Experience developing APIs for integrating AI models with backend or enterprise systems


• Understanding of Model Context Protocol (MCP) and AI system orchestration

Desirable Skills

• Experience with MLOps tools and practices for deployment and monitoring


• Familiarity with cloud platforms such as AWS, Azure, or GCP


• Experience working with Git and collaborative development workflows


• Strong communication skills and ability to collaborate with cross‑functional teams