Lead AI Security Automation Engineer
$120,000–$217,500 year
On-siteToronto, Ontario, Canada or Boston, Massachusetts, United States
Toronto, Ontario, Canada or Boston, Massachusetts, United StatesOn-siteFull Time$120,000–$217,500 yearSenior LevelEnterprise
Full TimeSenior LevelEnterprise
Job Summary
Build and lead a high-performing team of AI Automation Engineers to develop scalable, reliable agentic AI platforms for security workflows. Design production-grade systems including agents, skills, memory patterns, and guardrails while engineering retrieval and context approaches using embeddings and semantic search. Architect cloud-native solutions on AWS, Azure, and GCP using containers, serverless patterns, and event-driven messaging to optimize performance and cost. Establish evaluation, testing, and observability frameworks to ensure quality and safe AI operation, while mentoring senior and junior engineers through code reviews and architecture forums. Leverage enterprise-authorized AI coding tools to accelerate delivery and improve code quality across complex security deliverables.
Required Qualifications
- Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Cybersecurity, or a related discipline
- 10+ years of professional software engineering experience
- 4+ years of experience developing AI, Machine Learning, or Generative AI solutions
- Demonstrated success designing and deploying enterprise-scale applications and platforms
- Experience developing cybersecurity, analytics, or operational intelligence solutions
- Work shift is 8 AM – 5 PM local time with occasional Level 3 escalation resolution to support operational incidents
- This hybrid role includes an in-office presence requirement of 2–4 days per week
- Demonstrated experience architecting, developing, and deploying production-grade Generative AI and Large Language Model (LLM) based solutions, including agentic workflows, intelligent agents, and enterprise tool integration frameworks
- Strong software engineering fundamentals with expertise in designing and delivering cloud-native applications and services leveraging containers, serverless architectures, and modern public cloud platforms
- Proven experience building highly scalable distributed systems utilizing asynchronous processing, event-driven architectures, durable messaging, and high-performance data access patterns
- Hands-on expertise developing Retrieval-Augmented Generation (RAG) solutions, including embeddings, semantic search, knowledge grounding, context engineering, prompt optimization, and prompt lifecycle management
- Experience implementing AI evaluation, testing, monitoring, and observability frameworks to measure model quality, reliability, performance, and safe operation in production environments
- Strong API design and integration experience, including the development of secure, reusable, and scalable platform services that enable enterprise-wide adoption of AI capabilities
- Demonstrated technical leadership skills with a track record of mentoring engineers, driving architectural decisions, influencing technology strategy, and collaborating effectively with cross-functional stakeholders
- Hands-on experience utilizing enterprise-approved AI-assisted software development tools to accelerate application delivery, improve code quality, streamline testing, and enhance documentation, while ensuring outputs are validated through secure coding practices, peer review, and automated testing
- Strong understanding of responsible AI principles, including data privacy, security, governance, resiliency, and risk management, with the ability to guide teams in the safe and effective use of AI technologies
- Deep understanding of cybersecurity functions to support threat detection engineering, threat hunting, offensive/defensive security, Threat intelligence and SOC operations
- Strong understanding of cybersecurity operations, including threat detection, incident response, threat hunting, security analytics, and security automation
- Proven ability to lead and influence geographically distributed teams through virtual collaboration, fostering strong partnerships, driving technical outcomes, and building effective relationships across engineering, security, and business organizations
- Deep understanding of CI/CD tools (Jenkins, Harness, Spinnaker, Argo CD, etc.) and methodology, production experience in designing and implementing CI/CD pipelines with a focus on helping teams release frequently to production while maintaining deployment reliability
- Strong software development and automation skills with demonstrated experience in Python, JavaScript/TypeScript, Rust, Go (Golang), Bash, and PowerShell
- Proven ability to lead complex technical initiatives while remaining deeply hands-on
- LangGraph, Semantic Kernel, CrewAI, AutoGen, LangChain, or similar frameworks
- Databricks, Snowflake, Spark, Kafka, Delta Lake, Iceberg, and Airflow
- Experience with vector databases, semantic search, and enterprise RAG platforms
- Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks
- Knowledge of Responsible AI, data governance, and model risk management
Desired Qualifications
- Education & Preferred Qualifications
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