Datascientist

Key Responsibilities:

• 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

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, India, Central and Eastern Europe.

www.griddynamics.com