8+ years of software engineering experience, with at least 5 years in technical leadership roles and 4+ years focused on AI/ML systems in productionExpert software engineering background (Python or similar) with strong design sensibilities for scalable, maintainable systems
Deep, hands-on expertise designing and shipping production multi-agent agentic AI systems, including agent orchestration, planning, tool use, and multi-agent coordination patterns
Deep expertise with AWS, including in-depth knowledge of AWS GenAI offerings and hands-on experience with Amazon Bedrock AgentCore; broader multi-cloud experience (Azure, GCP) is a plus
Strong background in microservices, serverless, containers, and event-driven systems (e.g., Kubernetes, Docker, Lambda, EventBridge)
Proficiency with infrastructure as code and CI/CD (e.g., Terraform, CloudFormation, Pulumi, GitHub Actions)
Strong data architecture expertise across relational, NoSQL, and big data systems (e.g., PostgreSQL, MongoDB, Snowflake, BigQuery, Spark, Kafka)
Hands-on experience with data modeling, ETL/ELT pipelines, and orchestration (e.g., Airflow, Prefect, dbt)
Mastery of AI frameworks and orchestration tools for building agentic systems (e.g., LangChain, LangGraph, AgentCore, CrewAI, AutoGen, or equivalents)
Strong experience designing AI/ML systems for production, including LLMs, MLOps, and model serving (e.g., SageMaker, Bedrock, Vertex AI, MLflow, Hugging Face, PyTorch, TensorFlow)
Strong experience with evaluation frameworks and observability tools for LLM and agentic apps, including building these capabilities where they don’t yet exist
Deep understanding of AI safety, responsible AI principles, prompt injection defenses, and PII handling
Extensive experience building RAG pipelines: chunking strategies, embedding models, vector databases, and advanced retrieval techniques
API design experience, including architecting and integrating with internal and third-party services at scale
Advanced cost optimization expertise: token economics, caching strategies, model routing, quantization
Solid understanding of networking, security, identity, and access management in cloud environments
Experience with governance, compliance, and observability frameworks
Track record of senior technical leadership and mentoring experienced engineers
Strong stakeholder communication skills, with the ability to translate technical depth across audiences
Demonstrable, day-to-day usage and expert knowledge of AI-forward coding tools such as Claude Code and Cursor
Multi-cloud architecture experience, AI ethics or responsible AI experience, or enterprise architecture certifications (e.g., TOGAF, AWS/Azure/GCP) is a plus