GenAI / Agentic AI Engineer

Position 1:

Job Title: Lead GenAI / Agentic AI Engineer

Experience: 8 to 10 Years

Key Responsibilities:

• Lead the design and development of GenAI and Agentic AI-based applications using Python.
• Architect scalable and secure solutions integrating LLMs, vector databases, and orchestration frameworks.
• Collaborate with data scientists, ML engineers, and product teams to translate business needs into AI-driven solutions.
• Manage project timelines, resources, and deliverables across agile teams.
• Ensure adherence to best practices in AI development, MLOps, and cloud deployment.
• Stay updated with emerging trends in GenAI, multi-agent systems, and open-source AI tooling.

Required Skills & Technologies:

• Strong proficiency in Python and AI/ML libraries (e.g., LangChain, Hugging Face, OpenAI SDKs).
• Experience with Agentic AI frameworks and multi-agent orchestration.
• Hands-on experience with LLMs, RAG pipelines, vector databases (e.g., FAISS, Pinecone, Weaviate).
• Familiarity with Databricks, PySpark, and SQL for data engineering.
• Cloud experience with AWS and containerization tools (e.g., Docker, Kubernetes).
• Knowledge of CI/CD, Git, and DevOps best practices.
• Exposure to Scaled Agile environments.

Position 2:

Job Title: GenAI Developer Python & Agentic AI

Experience: 4 to 6 Years

Key Responsibilities:

• Develop and deploy GenAI applications using Python and modern AI frameworks.
• Build and optimize RAG pipelines, integrate LLMs, and manage vector databases.
• Collaborate with cross-functional teams including data scientists, DevOps, and product managers.
• Implement and maintain agentic AI workflows using orchestration tools.
• Ensure code quality, scalability, and performance of AI-driven applications.
• Stay updated with the latest advancements in GenAI and agentic systems.

Required Skills & Technologies:

• Strong programming skills in Python.
• Experience with LLM frameworks (e.g., LangChain, Hugging Face, OpenAI SDK).
• Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate).
• Understanding of RAG architecture, prompt engineering, and token optimization.
• Exposure to agentic AI frameworks and multi-agent orchestration.
• Experience with AWS, Databricks, PySpark, and SQL.
• Knowledge of Git, CI/CD pipelines, and Agile development practices.