About the Role

You will independently lead applied AI solutions from feasibility and architecture through production operation. You should bring strong depth in either computer vision/deep learning or GenAI/RAG, plus hands-on experience in an adjacent AI capability. The role combines individual contribution, system design, technical leadership, and stakeholder communication.

What You Will Own

• Lead technical discovery, feasibility assessment, architecture, evaluation planning, and risk identification.
• Lead rapid proof-of-concepts to validate feasibility, customer value, and delivery risk.
• Design full-stack, production-grade AI systems across data, models, retrieval, APIs, application integration, deployment, and observability, selecting technologies against measurable requirements.
• Own a primary track: computer vision systems such as training, video analytics, and edge/cloud deployment; or GenAI systems such as RAG, hybrid retrieval, reranking, and agent/tool orchestration.
• Optimize training and inference for quality, latency, throughput, and cost through quantization, pruning, batching, caching, or hardware-aware deployment where appropriate.
• Partner with platform and DevOps teams on experiment tracking, data/model versioning, CI/CD, observability, and release controls.
• Establish regression, drift, safety, monitoring, and rollback gates; lead incident investigation and preventive improvement across models, data, and services.
• Mentor engineers, review code and architecture, and communicate trade-offs to product, business, and client stakeholders, including during technical proposals.
• Deliver polished customer demonstrations that clearly communicate technical capability and business value.
• Contribute reusable architectures, reference implementations, and technical knowledge to the internal AI Lab.

Required Qualifications

• 4+ years of relevant AI/ML or software engineering experience, including ownership of production AI systems.
• Deep expertise in computer vision/deep learning or GenAI/RAG, with practical experience in at least one adjacent AI domain.
• Strong Python engineering across API/service design, automated testing, code review, performance troubleshooting, and maintainable codebases.
• Strong fundamentals across software engineering, ML, data, evaluation, APIs, and deployment, plus full-stack AI delivery experience and a technology-agnostic approach.
• Strong customer-facing communication, presentation skills, and professional presence to run clear, reliable demonstrations.
• Experience designing evaluation frameworks using domain-appropriate measures: precision, recall, F1 or mAP; Recall@K, MRR or nDCG; and LLM/agent quality, groundedness, task success, safety, latency, and cost.
• Hands-on cloud or on-premises GPU infrastructure experience, including Docker, CI/CD, and multi-service debugging.
• Ability to turn ambiguous business problems into technical options, acceptance criteria, and phased plans while guiding engineers and stakeholders through trade-offs.

Preferred Qualifications

• Model optimization, serving, or fine-tuning with Triton, TensorRT, ONNX, Jetson, LoRA/QLoRA, or comparable approaches.
• Advanced RAG, agent workflows, multilingual NLP, document AI, or OCR experience.
• Enterprise AI governance or client-facing solutioning experience, including access control, privacy, auditability, model risk, or presales.