· Overall experience should 13+ years in related field.
· 8+ Years of relevant Experience in Related Field
· Education: BS, EE or CS degree is a plus.
· Exceptional communication and people skills, with a passion for product excellence, talent development and mentoring.
· Knowledge of Build, Deployment, and Continuous Integration concepts and best practices, including full SDLC.
· Strong experience in Agile methodology, processes, and best practices.
· Experience in TDD concepts, methodologies, and best practices (i.e., mocks, spies, unit tests, code coverage, etc.). BDD experience is a plus.
· Experience with technical design and hands-on work within a modern OOP-based language (C#, Java, Python, etc.) and related stack.
· Experience in modern Database platforms, including such items as data modeling, stored procedures, schema definitions, performance and scaling considerations.
· Recent experience working with, troubleshooting and tuning across all layers of a modern SaaS, N-Tier, web-based application.
· Proven experience working with large-scale, high-performance systems, with a strong track record of addressing and solving scalability challenges in cloud-based environments.
· Understanding of QA standards and best practices, including automation, regression, and smoke testing, white/black-box testing, and related QA processes.
· Hands-on exposure to Agentic AI and Generative AI, including designing, building, and deploying multi-agent systems, agent orchestration, planning/decomposition of complex tasks, and tool-augmented or Agentic RAG workflows.
· Experience establishing agent governance — defining policies, guardrails, and deterministic boundary controls (e.g., budget limits, disallowed actions) to safely operate autonomous agents in production, along with Human-in-the-Loop (HITL) approval and escalation workflows.
· Familiarity with auditability and explainability practices for AI and agentic systems, including immutable audit logging and traceable, explainable decision trajectories to support compliance and risk review.
· Understanding of AI risk mitigation practices, such as defending against prompt injection and rogue planning, and implementing containment and circuit-breaker mechanisms for agents accessing sensitive data.
· Ability to define evaluation frameworks and production-readiness metrics (accuracy, cost, latency, coverage) for Agentic AI and GenAI solutions, and to guide engineers on evaluation contracts and component boundaries across agent memory, planning, and tool usage.