Data Scientist

Key Responsibilities:

• 4 - 7 years experience
• Design and implement end-to-end generative AI solutions including RAG chatbots, LLM-powered BI systems, and coding agents
• Develop and deploy AI agents using frameworks like LangGraph and similar orchestration tools
• Build robust data processing pipelines using LlamaIndex and related libraries for document ingestion and retrieval
• Implement and optimize vector databases for semantic search and retrieval systems
• Integrate multiple LLM providers (OpenAI, Gemini, Anthropic) and evaluate model performance
• Set up comprehensive observability and monitoring using tools like LangSmith
• Collaborate with engineering teams to productionize ML models and AI applications

Required Skills:

Generative AI (Minimum Requirements):

• Hands-on experience with LangGraph or similar AI agent frameworks
• Proficiency with LlamaIndex, LangChain, or equivalent data processing libraries
• Experience with vector databases (Pinecone, Weaviate, Chroma, etc.)
• Working knowledge of multiple LLMs and their APIs (OpenAI GPT, Gemini, Claude)
• Experience with LLM observability tools (LangSmith, Weights & Biases, etc.)
• Proven track record in building LLM BI solutions (natural language to SQL)
• Experience developing RAG (Retrieval-Augmented Generation) chatbots
• Experience with coding agents and code generation systems

Traditional AI/ML:

• Strong foundation in clustering, regression, classification, and forecasting
• Proficiency with scikit-learn, PyTorch, TensorFlow
• Experience with statistical analysis and experimental design
• Knowledge of feature engineering and data preprocessing techniques

Good to Have:

• Fine-tuning experience with LLMs, rerankers, or embedding models
• Self-hosting and deployment of open-source LLMs
• Experience with BERT, transformer architectures, or computer vision models (YOLO)
• MLOps experience with MLflow, Weights & Biases, or TensorBoard
• Cloud platform certifications (AWS, GCP, Azure)

Additional Requirements:

• Strong programming skills in Python
• Experience with containerization (Docker, Kubernetes)
• Knowledge of API development and microservices architecture
• Understanding of prompt engineering and prompt optimization techniques
• Experience with evaluation frameworks for LLM applications
• Familiarity with data privacy and security best practices for AI applications