Tech Lead

Job Description

Job Description

Title:
Lead AI Engineer / Data Scientist
Focus area:
GenAI, Agentic
Computer Vision Solutions
Location:
On-site (Santa Clara) day1 onsite
About the Role
We are looking for a Lead AI Engineer / Data Scientist: an AI-engineering-heavy practitioner who combines deep algorithmic and deep learning expertise with strong solutioning and customer-facing skills. This is a hybrid role: part senior AI engineer, part technical lead, and part trusted advisor to customers.
You will design and build production AI systems end to end across multiple domains: Generative and Agentic AI (including working with foundation models such as Claude), Computer Vision on unstructured data, forecasting, and optimization. Your work spans understanding the customer's problem and datasets, selecting the right algorithms, fine-tuning, and deploying models, architecting agentic workflows, and standing up the surrounding infrastructure. You will be the technical face of these solutions: guiding developers, resolving customer issues in real time, and translating ambiguous requirements into concrete, workable plans.
The ideal candidate has a strong track record of working with roughly 10 AI/ML projects deployed to production and is as comfortable writing production deep learning code as they are sitting in front of a customer diagnosing an issue and proposing a path forward.
Key Responsibilities
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.
Required Qualifications:
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.