Bachelor’s degree in computer science, engineering, or a related field; a Master’s degree is a plus
10+ years of experience in software or platform architecture, with at least 1 year of hands-on experience working with large language models (LLMs), AI platforms, or machine learning-driven systems
Demonstrated expertise in building or deploying AI agents or autonomous workflows, including a deep understanding of LLMs, prompt engineering, tool calling, and function execution
Proficiency in working with vector databases, embeddings, and retrieval-augmented generation (RAG) pipelines, with hands-on experience in distributed systems and APIs
Hands-on experience with cloud platforms, including Kubernetes, and familiarity with event-driven architectures or workflow engines such as Temporal, Airflow, or Step Functions
Familiarity with multi-agent orchestration frameworks like LangGraph, AutoGen, CrewAI, or Semantic Kernel is highly preferred
Experience in designing and implementing AI governance, security, or compliance programs, including the creation of guardrails for safety, reliability, and ethical AI practices
Knowledge of observability tools and practices for AI systems, such as prompt tracing, cost tracking, and failure analysis
Expertise in system architecture, workflow orchestration, and automation, with a focus on building scalable, secure, and reliable systems
Proven experience in designing and deploying event-driven or distributed systems, including working with message queues and real-time data flow use cases
Strong communication and collaboration skills to work effectively with cross-functional teams, including engineering, product, legal, and risk teams, ensuring alignment across technical and business priorities