Data Scientist

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) :

Strong programming skills in Python

Hands-on experience with Temporal, 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

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)

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

Additional Requirements :

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.

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