Main Responsibilities:
- Architect and develop production AI / agentic systems optimized for business use cases, including agent infrastructure and integrations with client infrastructure
- Manage AI product development roadmap
- Rapid testing and evaluation of agentic systems, including reliability engineering, performance measurement and cost management
- Continuously interface with clients to understand areas for transformation and co-design AI and security best practices
Required experience and qualifications:
- BS in Computer Science or closely-related field
- 3+ years of relevant industry experience or equivalent
- Direct experience shipping an AI or agentic AI system end-to-end to users, preferably owned or led the project
- Excellent communication skills, particularly in communicating technical concepts in an approachable way to clients
- Strong production maturity and system design skills
- Familiarity with agentic tooling and development, including Model Context Protocols (MCPs), agent skills, agent orchestration, Retrieval Augmented Generation (RAG), etc.
- Experience designing evals, observability and monitoring for AI, plus debugging failure modes in production
- Knowledge of enterprise security best practices to securely deploy AI and software
- Proficiency in building data pipelines and API integrations
- Familiarity with cloud platform (AWS (preferred)/Azure/GCP), infrastructure as code, containerization
- Passion for experimenting with AI models, open-source repos, etc.
- Knowledge of latest developments in AI capabilities and deployment strategies
Good to haves
- MS or PhD in Computer Science or related field - or equivalent depth from industry
- 5+ years of relevant industry experience or equivalent
- Experience deploying AI in a specific vertical or business use case
- Ability to identify and articulate business value from AI deployments - including ROI framing, process mapping, and stakeholder alignment
- Familiarity with enterprise software ecosystems, including Oracle SAP, Microsoft, etc.
- Proficiency in additional languages, such as TypeScript, SQL, Java, Go
- Familiarity with data privacy practices for AI
- Experience with on-device or on-prem data management or AI deployment.
- Familiarity with ML frameworks such as PyTorch, TensorFlow, etc.
