Job Summary

The Lead AI Engineer / Data Scientist is an AI-engineering-focused role combining advanced algorithmic and deep learning expertise with technical leadership and customer-facing solutioning. The role is responsible for designing and building production AI systems across Generative and Agentic AI, computer vision, forecasting, and optimization. The position involves understanding customer problems and datasets, selecting and fine-tuning appropriate models, architecting agentic workflows, deploying production solutions, and establishing supporting infrastructure. The role also provides technical leadership to developers and data scientists and serves as a trusted technical advisor to customers.

Key Responsibilities
• Design end-to-end ML, LLM, and agentic solutions from problem framing and data strategy through deployment and monitoring.
• Architect agentic systems, including multi-step, tool-using, and multi-agent workflows with orchestration, tool and function integration, memory, and guardrails.
• Own technical architecture decisions covering model selection, fine-tuning approaches, agent and orchestration design, serving strategy, API design, and infrastructure.
• Lead and mentor data scientists and developers and translate complex customer requirements into structured project plans with clear milestones and deliverables.
• Apply algorithmic fundamentals to select, adapt, and implement appropriate approaches across traditional machine learning, deep learning, Generative AI, and Agentic AI.
• Build deep learning models using unstructured data such as images, video, text, audio, and sensor or time-series data for production applications.
• Design and deploy production computer vision solutions, including detection, classification, segmentation, OCR, and tracking.
• Develop forecasting models for time-series, demand, and behavioral forecasting and integrate them into decision-making workflows.
• Work with foundation models such as Claude and other large language models for prompting, fine-tuning, evaluation, and integration.
• Apply RAG and tool-use patterns to develop AI solutions.
• Guide development teams in implementing scalable AI solutions and resolve technical issues in customer environments.
• Translate ambiguous customer requirements and technical challenges into practical solution plans.
• Support model deployment, monitoring, experimentation, and production optimization.
• Apply distributed training and inference optimization techniques, including quantization, batching, and GPU utilization.
• Design and implement AI solutions using containerization and orchestration technologies such as Docker and Kubernetes.

Required Qualifications
• Bachelor's and Master's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience.
• Strong experience in AI/ML engineering, algorithm development, and deep learning.
• Experience designing and deploying production AI/ML solutions across multiple projects and use cases.
• Experience with agentic AI frameworks and protocols such as LangGraph, LlamaIndex, AutoGen, CrewAI, or MCP.
• Experience with RAG and tool-use patterns.
• Experience with MLOps practices and tooling, including experiment tracking, ML CI/CD, model registries, and monitoring.
• Experience with distributed training and inference optimization, including quantization, batching, and GPU utilization.
• Experience with Docker and Kubernetes or similar containerization and orchestration technologies.
• Strong programming and production engineering skills for developing AI and deep learning solutions.
• Experience with deep learning frameworks such as PyTorch, TensorFlow, Keras, JAX, or PyTorch Lightning.
• Experience with Generative AI and model fine-tuning technologies such as Hugging Face Transformers, PEFT, LoRA/QLoRA, TRL, Accelerate, DeepSpeed, bitsandbytes, Axolotl, or Unsloth.
• Experience with model serving technologies such as vLLM, TGI, or Ollama.
• Experience with LLM orchestration and RAG frameworks such as LangChain or LlamaIndex.
• Experience with agentic AI technologies such as Claude Agent SDK, Anthropic or OpenAI SDKs, LangGraph, AutoGen, CrewAI, Semantic Kernel, or Model Context Protocol (MCP).
• Experience with tool and function calling and multi-agent patterns.
• Experience developing computer vision and image or video processing solutions using technologies such as OpenCV, Detectron2, or Segment Anything (SAM).
• Experience with forecasting and optimization technologies such as statsmodels, Prophet, GluonTS, Darts, scikit-learn, OR-Tools, SciPy, PuLP, Gurobi, or CVXPY.
• Strong technical communication and customer-facing skills, with the ability to explain complex technical concepts and recommend practical solutions.
• Ability to lead technical initiatives, mentor team members, and work effectively across customer, engineering, and data science teams.

Notes:

Must Have Skills

Skill 1 - Design end-to-end ML/LLM and agentic solutions, from problem framing and data strategy through to deployment and monitoring

Skill 2 - Architect agentic systems: multi-step, tool-using, and multi-agent workflows: including orchestration, tool/function integration, memory, and guardrails

Skill 3 - Work with foundation models including Claude and other LLMs: prompting, fine-tuning, evaluation, and integration

Good To have Skills -

Skill 1 - Familiarity with MLOps tooling (experiment tracking, CI/CD for ML, model registries, monitoring).

Mandatory if Applicable

Domain Experience (If any) - Lead AI Engineer / Data Scientist

Toolkit Management

Must have Certifications - NA

Location - Santa Clara, CA

Onsite Requirement - Yes

Number of days onsite - 5 Days