Key Responsibilities:
- Support and contribute to Generative AI initiatives across the organization.
- Develop and deploy GenAI applications using LLMs and AI frameworks.
- Leverage models such as GPT, Gemini, and LLaMA for AI-powered solutions.
- Build and manage multi-agent AI systems.
- Develop solutions using LangChain, LangGraph, FastAPI, Streamlit, Chainlit, and Transformers.
- Integrate AI capabilities into enterprise applications through APIs and cloud-native architectures.
- Design and implement RAG pipelines using vector databases such as FAISS, Qdrant, Weaviate, and ChromaDB.
- Collaborate with stakeholders to understand requirements and deliver AI-driven business solutions.
- Provide analysis, insights, and recommendations to support business needs.
- Stay current with emerging Generative AI technologies, tools, and best practices.
Key Requirements:
- Bachelor's degree in computer science, Engineering, Statistics, or a related technical field
- 6 - 8 years of experience in Data Analytics, Data Science, or Generative AI roles.
- Strong experience delivering data-driven and AI solutions in production environments.
- Proficiency in Python, PySpark, and SQL.
- Hands-on experience with data science libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib.
- Experience with GenAI platforms and tools, including OpenAI, Anthropic, Google Vertex AI, FastAPI, Gradio, and Streamlit.
- Strong understanding of RAG (Retrieval-Augmented Generation) and Agentic AI architectures using LangChain and Lang Graph.
- Experience developing and deploying APIs using Fast API or similar frameworks.
- Knowledge of MLOps practices and machine learning deployment workflows is a plus.
- Strong analytical, problem-solving, communication, and stakeholder management skills.