Description :

5 to 10 years overall work experience, with at least 1 year in Generative AI.

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.

Requirements :

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.

Nice to have :

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.

We offer :

- Opportunity to work on bleeding-edge projects.

- Work with a highly motivated and dedicated team.

- Competitive salary.

- Flexible schedule.

- Benefits package medical insurance, sports.

- Corporate social events.

- Professional development opportunities.

- Well-equipped office.

About Us:

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services.

Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation.

A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience.

Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.