Python with Agentic AI & GenAI Engineer

smartrecruiters

chennai 5 Years Exp Posted 72d ago

Job Description

We are looking for a skilled  and delivery-focused Agentic AI & GenAI Engineer, who is passionate about developing innovative solutions using Agentic AI frameworks. The ideal candidate should have experience in designing, implementing, and optimizing Agentic workflow and building scalable, production-grade Agentic AI models.

Key Responsibilities:

  • Develop, test, and manage day-to-day execution of AI projects, ensuring timely delivery, quality, and alignment to architectural design.
  • Translate architectural blueprints into executable components, prototypes, and production-ready services.
  • Oversee the development of GenAI applications, RAG pipelines, and agent-based solutions using frameworks like Langchain, LangGraph and Google ADK.
  • Work on data preprocessing, feature engineering, and model optimization.
  • Optimize performance of existing Agentic AI models and pipelines.
  • Review code, support reusable component development, and enforce engineering best practices.
  • Utilize cloud-based services (AWS, Azure, or GCP) for model deployment and scaling.
  • Stay updated with the latest advancements in AI, ML, and data science.
  • Debug, troubleshoot, and resolve performance bottlenecks in applications.

Qualifications

Qualifications:

  • Graduation/Post-Graduation in Science/Engineering.
  • 5-8 years of experience in Python development 
  • Strong proficiency in AI/ML engineering or software development using Python and its frameworks.
  • Experience delivering projects using GenAI, LLMs, or Transformer models and Agentic AI based solutions.
  • Solid understanding of prompt engineering, APIs, data preprocessing, and model integration.
  • Exposure to vector databases and RAG architectures for enterprise knowledge retrieval. Exposure to MCP and Agent to Agent Communication
  • Familiarity with SQL, NoSQL, and data warehousing solutions and ability to analyze complex datasets and derive meaningful conclusions.
  • Understanding of CI/CD pipelines, Docker, and Kubernetes for AI model deployment

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