Lead AI Engineer

Job Title Pay Range: $55.00hr - $60.00hr Requirement/Must Have 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. Experience with distributed training and inference optimization (quantization, batching, GPU utilization). Exposure to containerization and orchestration (Docker, Kubernetes). Strong track record of working with roughly 10+ AI/ML projects deployed to production. Deep algorithmic and deep learning expertise with strong solutioning and customer-facing skills. Responsibilities 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. 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. Nice to Have Familiarity with MLOps tooling (experiment tracking, CI/CD for ML, model registries, monitoring). Skills AI. ML. LLM. Claude. PyTorch. TensorFlow. Keras. JAX. PyTorch Lightning. Hugging Face Transformers. PEFT (LoRA/QLoRA). TRL. Accelerate. DeepSpeed. bitsandbytes. Axolotl. Unsloth. vLLM. TGI. Ollama. LangChain. LlamaIndex. Claude Agent SDK. Anthropic / OpenAI SDKs. LangGraph. AutoGen. CrewAI. Semantic Kernel. Model Context Protocol (MCP). OpenCV. Detectron2. Segment Anything (SAM). Prophet. GluonTS. Darts. scikit-learn. OR-Tools. SciPy. PuLP. Gurobi/CVXPY. Docker. Kubernetes. Qualification And Education Strong verbal and written communication skills. Excellent communication and presentation skills. Ability to communicate effectively with stakeholders. Cynet Systems