About The Client

Location- Bengaluru, India

Our client is an innovative legal‑tech company focused on transforming corporate sustainability. Their AI‑enabled ESG platform simplifies the entire sustainability workflow, automating data collection, managing over 1,000 ESG KPIs, providing analytics and benchmarking, and generating compliant reports aligned with frameworks like GRI, CSRD, TCFD, and more.

They are seeking a AI/ML Engineer to join their data and machine-learning team. In this role, they will contribute to developing and deploying machine-learning models that underpin the platform’s advanced ESG analytics, automation, and AI-assurance features. They will collaborate across teams working with data scientists, engineers, and product experts helping refine features from data engineering to model deployment and monitoring. This opportunity is ideal for someone early in their ML career who is eager to gain hands-on experience and deliver real-world impact.

Key Responsibilities

• Design, deploy, and maintain AI/ML infrastructure that can scale to meet business needs.
• Build and manage CI/CD pipelines for model training, validation, and deployment using tools such as Terraform, CloudFormation, Azure DevOps, or GitHub Actions, along with Kubeflow or MLflow.
• Containerize model serving workloads using Docker and orchestrate them with Kubernetes (EKS/AKS), while implementing canary or blue/green deployment strategies and autoscaling.
• Implement observability around AI workloads including model drift detection, versioning, performance monitoring, alerting (CloudWatch/Azure Monitor), and compliance auditing.
• Optimize cost and performance of AI infrastructure, including GPU/CPU resource allocation, distributed training, and spot/pre-emptible instance usage.
• Lead technical reviews of model selection, training strategies (transfer learning, hyperparameter tuning), and pipeline reliability.
• Collaborate closely with data scientists, back-end engineers, product owners, and security teams to align infrastructure with business goals and regulatory standards.
• Serve as an AI/cloud technical lead or mentor, guiding junior engineers on cloud architecture, MLOps best practices, and troubleshooting.
• Contribute to infrastructure automation and tool development—SDKs, CLI wrappers, and reproducible pipelines.

Requirements

• 1-5 years of hands-on experience deploying AI/ML systems into production, with at least 3 years using AWS and/or Azure cloud platforms.
• Deep proficiency in Python and popular ML packages (TensorFlow, PyTorch, Scikit-Learn), along with performance optimization techniques.