AI Orchestration Engineer, Cybersecurity - Assistant Vice President
State Street
- Location
- Bangalore
- Employment
- Full Time
- Work model
- On-Site
- Level
- Staff
- Posted
- Aug 14, 2026
Skills
About this role
Who we are looking for: The State Street Global Cybersecurity (GCS) organization is seeking an AI Orchestration Engineer to help build the next generation of enterprise AI capabilities across Cybersecurity functions. As part of the GCS team, you will operate at the intersection of Artificial Intelligence, Agentic Systems, Data Engineering, and Enterprise Governance. This role is responsible for designing, engineering, and operationalizing scalable AI orchestration frameworks that transform enterprise data into intelligent, auditable, and secure business outcomes. This role requires strong capabilities in both AI Engineering and Data Engineering. You will design data products, orchestrate multi-agent workflows, develop Retrieval-Augmented Generation (RAG) systems, integrate enterprise knowledge sources, and establish the governance and observability capabilities required to operate AI safely within a highly regulated financial services environment. What you will be responsible for: Design and engineer AI orchestration frameworks that coordinate multiple models, agents, tools, APIs, and enterprise applications. Develop agent-to-agent and human-in-the-loop workflows that automate complex operational and analytical processes. Build reusable orchestration patterns that enable rapid deployment of AI-enabled business capabilities. Design and develop scalable data pipelines supporting AI, analytics, and agentic workflows. Build enterprise data products optimized for AI consumption. Design and implement enterprise RAG architectures. Develop reusable AI platform components supporting multiple use cases and business domains. Implement MLOps and LLMOps deployment, monitoring, versioning, and governance capabilities. Implement Responsible AI guardrails, governance controls, and model risk management processes. Build AI observability, evaluation, telemetry, and performance measurement solutions.
What we value
State Street is accelerating the adoption of AI-enabled capabilities to improve operational efficiency, enhance cybersecurity resilience, strengthen risk management, and deliver intelligent experiences across the enterprise. As an AI Orchestration Engineer, you will help establish the AI execution layer that enables secure collaboration between enterprise data platforms, large language models, internal knowledge repositories, agentic workflows, governance controls, and human decision makers . Mandatory skills / experience:
Minimum Qualifications
Bachelor's degree in Computer Science, Data Engineering, Information Systems, Artificial Intelligence, or equivalent practical experience. 10 - 15 years of experience in Data Engineering, Machine Learning Engineering, Software Engineering, or related disciplines. 3–5 years of experience in AI Engineering – such as Prompt Engineering, Gen AI, Agentic AI, MCP Framework, RAG Architecture or related disciplines. Strong experience developing large-scale data pipelines and distributed data-processing solutions. Experience with Python, SQL, APIs, workflow automation, and cloud-native architectures. Strong communication and stakeholder collaboration skills. Additional requirements / Good to have skills:
Preferred Qualifications
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. Mode of work: Hybrid Additional information (if any): Accelerate delivery of AI-enabled business capabilities through reusable orchestration frameworks. Increase adoption of governed enterprise AI services. Improve enterprise data accessibility for AI use cases. Enhance reliability, observability, and auditability of AI systems. Reduce