AI / ML Engineer - RDE

We are looking for experienced AI/ML Engineers with strong Python expertise to build and deploy enterprise-grade agentic AI and GenAI solutions. The role involves developing intelligent agents, integrating LLM-based applications with enterprise platforms, and implementing scalable AI solutions across cloud-native environments.

Responsibilities:

• Design and develop Agentic AI / GenAI applications using Python.
• Build and integrate LLM-powered agents, RAG pipelines, and AI workflows.
• Develop prompt engineering, tool/function calling, and agent orchestration solutions.
• Build and integrate REST APIs and enterprise applications/platforms.
• Deploy AI solutions on AWS / Azure / GCP cloud platforms.
• Implement CI/CD, DevOps, and cloud-native deployment practices.
• Develop testing frameworks and perform AI/LLM application validation.
• Implement observability, monitoring, logging, and performance tracking for AI systems.
• Apply security, responsible AI, governance, and access-control practices.
• Collaborate with product, engineering, and business teams to deliver enterprise AI solutions.

Requirements:

• Candidates should have strong software engineering fundamentals along with practical experience building AI/ML and agentic AI solutions.
• Candidates with hands-on experience in taking GenAI/LLM applications from development through integration, deployment, testing, and production monitoring are preferred.

Mandatory Skills:

• Strong hands-on Python development experience.
• Experience in AI/ML and generative AI.
• Hands-on experience with Agentic AI / AI Agents.
• Strong understanding of LLMs, prompt engineering, and RAG.
• Experience with REST APIs / API integration.
• Experience with at least one cloud platform: AWS, Azure, or GCP.
• Knowledge of CI/CD and DevOps practices.
• Experience with enterprise application/platform integration.
• Understanding of AI testing, observability, and monitoring.

Good to Have:

• LangChain / LangGraph or similar agentic AI frameworks.
• Vector databases and embedding technologies.
• LLM APIs such as Azure OpenAI, OpenAI, Amazon Bedrock, or Vertex AI.
• Docker / Kubernetes.
• MLOps / LLMOps.
• AI security, responsible AI, and governance.
• Enterprise integration platforms and API gateways.