Senior AI Security Automation Engineer

State Street

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Quincy, MAToronto, Ontario, CanadaPrinceton, NJBoston, MAClifton, NJ
Salary: $120,000–$202,500 / yr
Employment: Full-time
Posted: 1 day ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $180k

This role $161k

$109k most similar roles pay here $224k

This role pays less than 59% of similar roles. Most pay $147,818–$213,125 — the shaded band above. At the midpoint, this role pays about $161k versus about $180k for comparable roles.

Based on 240 similar postings.

Employer

About State Street

State Street Corporation is one of the world''s largest custodian banks and asset managers, providing investment servicing, investment management, and investment research to institutional investors. Industry: Financial Services & Asset Custody

State Street currently has 267 open roles on FindRole.

Listed pay typically runs $120,000–$202,500 across 190 roles with salary data.

Most-posted roles

View all roles at State Street

At a glance

TL;DR · Senior AI Security Automation Engineer

Role Posting Log in to save

Senior AI Security Automation Engineer joins the Cyber Data & Analytics team to develop next-generation AI platforms, autonomous agents, and security automation solutions. This role focuses on building production-grade systems featuring agentic workflows, memory patterns, guardrails, and retrieval-augmented generation techniques like semantic search and prompt management. The engineer will design cloud-native services across AWS, Azure, and GCP using containers, serverless patterns, and distributed data stores to enhance cyber resilience and automate security operations. Key technologies include Python, JavaScript/TypeScript, Rust, Go, Bash, and PowerShell, alongside frameworks like LangChain or CrewAI. Data tools include Databricks, Snowflake, Spark, Kafka, and various vector databases. The role addresses the challenge of protecting against sophisticated global threat actors by integrating AI into cybersecurity workflows, utilizing SIEM platforms, and managing large-scale security data to improve detection and response capabilities.

Skills

Generative AI LLM RAG LangChain LangGraph Semantic Kernel CrewAI AutoGen Python JavaScript TypeScript Rust Go Bash PowerShell AWS Azure GCP Serverless Kafka Spark Databricks Snowflake Airflow CI/CD Jenkins Harness Spinnaker Argo CD Vector Databases SIEM

What you'll do

Architect and deliver scalable, production-grade agentic AI platforms for security workflows.
Design and build AI systems including agents, memory patterns, guardrails, and tool-use orchestration.
Develop retrieval and context-engineering solutions using embeddings, semantic search, and prompt management.
Engineer cloud-native AI services across AWS, Azure, and GCP using containers and serverless patterns.
Build governed APIs to integrate AI capabilities with security platforms and business processes.
Establish evaluation, regression testing, and observability frameworks to improve agent behavior and quality.
Define engineering standards for reliability, security, and safe AI operation throughout the platform lifecycle.
Mentor junior engineers and lead technical direction through code reviews and architecture forums.

What we're looking for

Bachelor's or Master's degree in Computer Science, Software Engineering, AI, Data Science, Cybersecurity, or a related field.
7+ years of professional software engineering experience.
3+ years of experience developing AI, Machine Learning, or Generative AI solutions.
Experience designing and deploying enterprise-scale applications and platforms.
Experience developing cybersecurity, analytics, or operational intelligence solutions.
Proficiency in Python, JavaScript/TypeScript, Rust, Go, Bash, and PowerShell.
Expertise in cloud-native development (AWS, Azure, GCP), containerization, and serverless architectures.
Experience with RAG, LLM frameworks (LangChain, CrewAI, etc.), vector databases, and MLOps/LLMOps.

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