
GenAI / Agentic AI Engineer
3 days ago
Position 1:
Job Title: GenAI Developer – Python & Agentic AI
Location: Pune – Kharadi
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.
Position 2:
Job Title: Lead GenAI / Agentic AI Engineer
Location: Pune – Kharadi
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.
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