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.