Python with Agentic AI & GenAI Engineer
smartrecruiters
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