Description

You will architect and deliver enterprise-grade AI solutions across Core ML, GenAI, and Agentic AI initiatives.

This role is hybrid.

Responsibilities

• Design and build scalable AI systems using Python, LLMs, RAG, and agentic workflows.
• Deploy and manage models using Docker, Kubernetes, and CI/CD pipelines.
• Implement MLOps practices with tools like MLflow, Kubeflow, or SageMaker.
• Mentor junior engineers and drive technical decisions for AI adoption.
• Ensure solutions meet governance, compliance, and security standards.

Required Skills

• 8+ years of software engineering experience with strong Python backend skills.
• Deep expertise in GenAI: LLMs, RAG, prompt engineering, and fine-tuning.
• Experience building agentic AI or tool-using LLM systems.
• Cloud proficiency in AWS, Azure, or GCP.
• Strong DevOps/MLOps experience with Docker, Kubernetes, and CI/CD.
• Familiarity with ML lifecycle tools (MLflow, Kubeflow, SageMaker, Azure ML).
• Experience with enterprise data platforms and ETL pipelines.
• Familiarity with monitoring tools like Splunk, Datadog, or AppDynamics.

Preferred Skills

• Experience in secure, regulated enterprise environments.
• Exposure to Jira and ServiceNow for operational workflows.

Key Skills

Education

Any Graduate