Grid Dynamics - Data Scientist

Key Responsibilities:

- 5 - 10 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