What are the responsibilities and job description for the Lead AI Engineer position at Oteemo Inc.?
Lead AI Engineer
Experience Level: 8 years
Location: Richmond, VA
Work Authorization:
Overview
Lead the design, development, and deployment of scalable, production-grade AI solutions. Own end-to-end AI architecture, ensure ethical and reliable AI delivery, and drive business impact through cross-functional collaboration and technical leadership.
Key Responsibilities
AI Architecture & Delivery
- Architect and oversee end-to-end AI/ML systems from PoC to production.
- Ensure high-performance models (low latency, high accuracy) and scalable inference.
- Guide implementation across Deep Learning, NLP, Computer Vision, Generative AI, and LLM-based solutions.
Production & MLOps
- Lead deployment of AI services using APIs, cloud platforms, and containerized environments.
- Establish CI/CD, model versioning, monitoring, and reliability standards (SLAs, uptime).
- Govern MLOps platforms, experiment tracking, feature stores, and model lifecycle management.
Generative AI & LLM Leadership
- Drive adoption of LLMs, RAG architectures, and agentic workflows.
- Define best practices for embeddings, vector search, semantic retrieval, and automation use cases.
Governance & Ethical AI
- Ensure bias detection, explainability, compliance, and AI governance adherence.
- Maintain auditability, documentation, and responsible AI standards.
Collaboration & People Leadership
- Partner with product, data, and engineering teams to translate business needs into AI solutions.
- Mentor engineers, conduct design reviews, and lead technical decision-making.
- Communicate complex AI concepts clearly to technical and business stakeholders.
Required Experience & Skills (Lead-Level)
- 5 years delivering and scaling production AI/ML systems with business impact.
- Strong system design, distributed processing, and cloud-native architecture experience.
- Expertise in Python and modern ML/DL frameworks (PyTorch, TensorFlow).
- Proven experience with MLOps, cloud AI platforms, and Kubernetes-based deployments.
- Hands-on exposure to LLMs, RAG frameworks, vector databases, and agent frameworks.
- Strong problem-solving, communication, and technical leadership skills.