GenAI/Agentic AI Engineer

Roles & Responsibilities

• Should be able to work independently with minimal directions and guide the team.
• Researching, learning, and applying new tools and techniques rapidly and suggesting new concepts to improve performance.
• Recognize the current application infrastructure and suggest new concepts to improve performance.
• Design and build production grade and secure GenAI and Agentic AI applications.
• Produce reusable, efficient, and scalable programs, and also cost-effective strategies.
• Develop AI pipelines and integrate with the data in Databricks and leverage different AWS services, including S3, EC2, API Gateway, Bedrock and Lambda.
• Comfortable to work on tight timelines, when required.

Skills Required

• Good decision-making and problem-solving skills.
• Strong expertise in Python programming with specialization using various GenAI and Agentic libraries (LangChain, LangGraph, Pydantic, Strands, CrewAi etc.,).
• Familiarity of Databricks fundamentals/architecture and AWS Bedrock, EC2, Postgress and other key services.
• Solid and proven experience in building GenAI and Agentic AI applications using LLM.
• Solid knowledge of GenAI components such as RAG, Vector Database, Model selection, Model evaluations, Guardrails etc.,
• Good Understanding of GenAI/Agentic AI model architecture best practices
• Hands-on experience in different domains, like database architecture, artificial intelligence, advanced analytics, big data, etc.

Nice to Have

• Knowledge of Databricks, Apache Airflow.
• Knowledge of Machine Learning.
• Solid knowledge on CI/CD pipelines in AWS technologies.
• Finance and Compliance domain knowledge.

Requirements

• Good decision-making and problem-solving skills.
• Strong expertise in Python programming with specialization using various GenAI and Agentic libraries (LangChain, LangGraph, Pydantic, Strands, CrewAi etc.,).
• Familiarity of Databricks fundamentals/architecture and AWS Bedrock, EC2, Postgress and other key services.
• Solid and proven experience in building GenAI and Agentic AI applications using LLM.
• Solid knowledge of GenAI components such as RAG, Vector Database, Model selection, Model evaluations, Guardrails etc.,
• Good Understanding of GenAI/Agentic AI model architecture best practices
• Hands-on experience in different domains, like database architecture, artificial intelligence, advanced analytics, big data, etc.

Benefits

Standard fulltime benefits