Job Description

Generative AI (Minimum Requirements):

Strong understanding of LLMs, agent architectures, generative pipelines. Proven experience in delivering AI solutions in enterprise setting. Hands-on with backend engineering: Python/Node.js, &REST APIs. Familiar with prompt engineering, model fine-tuning, and retrieval-augmented generation (RAG) Excellent communication skills with business and technical stakeholders 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.) 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 Understanding of multimodal AI.

Good to Have:

Experience onAWS Bedrock/SageMaker 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 AWS Cloud platform certifications Fine-tuning tools: LoRA, QLoRA, PEFT Experience in building LLM BI solutions (natural language to SQL)

5. Nice to have requirements to the candidate

Prior experience working on projects with a lot of PII data or working in Financial Services industry is a "Plus". Experience with

multimodal AI systems

combining text, image, and speech Familiarity with

AI Ethics, Safety

, and

Responsible AI Frameworks

Contributions to open-source AI projects or community involvement

Responsibilities

Generative AI (Minimum Requirements):

Strong understanding of LLMs, agent architectures, generative pipelines. Proven experience in delivering AI solutions in enterprise setting. Hands-on with backend engineering: Python/Node.js, &REST APIs. Familiar with prompt engineering, model fine-tuning, and retrieval-augmented generation (RAG) Excellent communication skills with business and technical stakeholders 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.) 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 Understanding of multimodal AI.

Requirements

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 Understanding of multimodal AI.

Nice to have

Good to Have:

Experience onAWS Bedrock/SageMaker 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 AWS Cloud platform certifications Fine-tuning tools: LoRA, QLoRA, PEFT Experience in building LLM BI solutions (natural language to SQL)

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.