Responsibilities:
- Lead the design, development, and evolution of Enterprise AI systems, taking it from vision to implementation, enabling production-ready AI capabilities.
- Lead and manage a team of software engineers responsible for developing GenAI apps and AI Agents.
- Architect multi-agent systems that orchestrate between different models, tools, and data sources
- Partner with Product, Research, and Infrastructure teams to define requirements, navigate tradeoffs, and ship multi-quarter initiatives that move core company metrics.
- Create frameworks and guardrails that enable fast, safe iteration on agent behavior, evaluation, and rollout.
- Embrace and drive a culture of accountability for customer and business outcomes.
- Build systems with a product approach for safety and governance: controls, audit ability, and risk management
- Providearchitectural guidance andestablishstandards for observability implementations with a strong focus on cost optimization and compliance.
- Collaborate with cross-functional teams to drive business impact and customer satisfaction through exceptional software delivery.
Required Qualifications, Capabilities & Skills:
- 5+ years of experience in software engineering and 2+ years managing software engineering teams.
- Have strong technical depth and Architect mindsetthat enables you to guide architecture, debug complex failures, and make trade-offs for efficiency.
- Have experience building distributed systems or high scale systems where reliability matters.
- Possess deep expertise in modern software engineering practices and principles, including AI/ML/GenAI, Agile methodologies.
- Experience with LLMs in solving real-world problems and building agentic AI applications.
Experience with agentic frameworks such as LangGraph. - Experience with agentic frameworks, runtimes, orchestration and evaluation/benchmarking systems.
- Experience with evaluation, experimentation, or model quality measurement systems.
- Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure.
Preferred Qualifications, Capabilities & Skills:
- Experience implementing enterprise-grade governance for agent systems in production autonomous workflows
- Experience in building and leading teams in the AI Infrastructure domain - LLM workflows, agentic systems, retrieval and evaluation
