Senior AI/ML Engineer

Senior AI/ML Engineer

Location: Hyderabad

Experience: 6+ Years

Employment Type: Full-Time

Job Summary

We are looking for a highly skilled Senior AI/ML Engineer to design, build, deploy, and scale enterprise-grade Machine Learning and Artificial Intelligence solutions that deliver measurable business value.

The ideal candidate will have strong hands-on expertise in Machine Learning, MLOps, cloud platforms, ML model deployment, scalable ML pipelines, feature engineering, model monitoring, and software engineering practices . The candidate will work closely with Data Scientists, Data Engineers, Platform Engineers, SRE, DevOps, and Product teams to take AI/ML solutions from experimentation to reliable production systems.

This role requires a strong engineering mindset with the ability to build scalable, secure, observable, and maintainable ML solutions across the complete machine learning lifecycle.

Key Responsibilities

Machine Learning & AI Solution Development

• Design, develop, deploy, and scale machine learning and AI solutions for enterprise use cases.
• Translate business and product requirements into scalable AI/ML solutions.
• Work closely with Data Scientists to productionize machine learning models.
• Select appropriate ML algorithms, frameworks, and architectures based on business requirements.
• Develop reusable components and frameworks for AI/ML applications.
• Optimize models and ML applications for accuracy, performance, scalability, and cost.

ML Model Deployment & Productionization

• Deploy machine learning models into production environments across cloud and enterprise platforms.
• Build scalable real-time and batch inference solutions.
• Develop model-serving APIs and microservices.
• Implement model versioning, release management, rollback, and deployment strategies.
• Support production ML models and troubleshoot performance or reliability issues.
• Ensure models meet production SLAs for availability, latency, and throughput.

MLOps

• Design and implement end-to-end MLOps pipelines for model development, validation, deployment, and monitoring.
• Implement automated CI/CD pipelines for ML models and applications.
• Establish repeatable and reproducible ML workflows.
• Automate model training, testing, validation, deployment, and retraining processes.
• Implement model registry and experiment tracking solutions.
• Establish best practices for model lifecycle management and governance.

ML Pipeline Development

• Design scalable and reliable data and ML pipelines.
• Build automated workflows for data preparation, feature engineering, model training, and inference.
• Integrate data pipelines with ML workflows.
• Implement data validation and quality checks.
• Optimize pipelines for large-scale datasets and distributed processing.
• Support both batch and real-time ML processing requirements.

Feature Engineering & Feature Management

• Design and implement scalable feature engineering frameworks.
• Develop reusable feature transformation pipelines.
• Ensure consistency of features between training and production environments.
• Implement feature versioning and lifecycle management.
• Work with Data Engineering teams to build reliable feature pipelines.
• Exposure to Feature Store technologies is preferred.

Model Monitoring & Observability

• Design and implement comprehensive monitoring solutions for production ML models.
• Monitor:
• Model accuracy
• Data quality
• Data drift
• Concept drift
• Model performance
• Prediction latency
• Infrastructure health
• Develop dashboards, alerts, and operational metrics.
• Implement observability across ML pipelines and model-serving infrastructure.
• Identify model degradation and implement appropriate remediation strategies.

Cloud & Infrastructure

• Design and deploy AI/ML solutions on cloud platforms such as AWS, Azure, or GCP .
• Work with cloud-native services for ML, compute, storage, networking, and monitoring.
• Build scalable and highly available ML infrastructure.
• Implement secure cloud architectures for AI/ML workloads.
• Optimize cloud infrastructure and ML workloads for performance and cost.
• Exposure to Infrastructure as Code tools such as Terraform or CloudFormation is preferred.

Software Engineering

• Develop production-quality code primarily using Python .
• Build scalable APIs and microservices for AI/ML applications.
• Follow clean coding, design, testing, and documentation pract…