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# Lead Data Scientist

- **Company:** Nityo Infotech Corporation
- **Location:** Santa Clara, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Continuous Integration, Distributed Computing Environment, Machine Learning, OpenCV, Tensorflow, SciPy, Unstructured Data, Pytorch, Prophet, Large Language Models, Multi-Agent Systems, Deep Learning, Keras, Data Strategy, Containerization, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Machine Learning Operations, Virtual Agents, Api Design, Docker
- **Published:** September 15, 2026
- **Apply:** https://www.dice.com/job-detail/6aaedb31-8ef6-41f5-a400-d313ea951991

## About the Role

* Bachelor's & Master's degree in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience).

 * Experience in agentic frameworks and protocols (e.g., LangGraph, LlamaIndex, AutoGen, CrewAI, MCP) and with RAG and tool-use patterns.

 * Familiarity with MLOps tooling (experiment tracking, CI/CD for ML, model registries, monitoring).

 * Experience with distributed training and inference optimization (quantization, batching, GPU utilization).

 * Exposure to containerization and orchestration (Docker, Kubernetes).

Technical Frameworks & Toolkit:

Deep Learning frameworks: PyTorch, TensorFlow, Keras, JAX; PyTorch Lightning.

GenAI & fine-tuning frameworks: Hugging Face Transformers, PEFT (LoRA/QLoRA), TRL, Accelerate, DeepSpeed, bitsandbytes, Axolotl, Unsloth; vLLM / TGI / Ollama for serving; LangChain, LlamaIndex for RAG and orchestration.

Agentic AI frameworks & protocols: Claude Agent SDK, Anthropic / OpenAI SDKs, LangGraph, AutoGen, CrewAI, Semantic Kernel, and the Model Context Protocol (MCP); tool/function calling and multi-agent patterns.

Computer vision: OpenCV, Detectron2, Segment Anything (SAM); image/video pipelines.

Forecasting & optimization: stats models, Prophet, GluonTS, Darts, scikit-learn; optimization/solver tooling (e.g., OR-Tools, SciPy, PuLP, Gurobi/CVXPY).

MLOps & infra: experiment tracking (MLflow / Weights & Biases), Docker, Kubernetes, CI/CD for ML, model registries and monitoring.

## Description

Solution Design & Technical Leadership:

 * Design end-to-end ML/LLM and agentic solutions, from problem framing and data strategy through to deployment and monitoring.

 * Architect agentic systems: multi-step, tool-using, and multi-agent workflows: including orchestration, tool/function integration, memory, and guardrails.

 * Own the technical architecture for solutions: model selection, fine-tuning approach, agent/orchestration design, serving strategy, API design, and infrastructure footprint.

 * Lead and mentor a team of data scientists and developers, break complex, ambiguous customer requirements into structured project plans with clear milestones and deliverables.

AI/ML Engineering & Modeling:

 * Apply strong algorithmic fundamentals to select, adapt, and implement the right approach for each problem: classic ML, deep learning, or generative/agentic AI.

 * Build deep learning models on unstructured data (images, video, text, audio, sensor/time-series) for real-world production use.

 * Design and ship computer vision solutions (detection, classification, segmentation, OCR, tracking, etc.) at production quality and scale.

 * Develop forecasting models (time-series and demand/behavioral forecasting) and integrate them into decisioning workflows.

 * Work with foundation models including Claude and other LLMs: prompting, fine-tuning, evaluation, and integration.