AI Orchestration Engineer, Cybersecurity - Assistant Vice President

Overview

Design, engineer and operationalize scalable AI orchestration frameworks that turn enterprise data into intelligent, auditable and secure business outcomes across Cybersecurity functions in a regulated financial services environment.

What you'll do

• 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 you'll need

• 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.

Nice to have

• 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.

Details

• Location: Bangalore, India.
• Work mode: Hybrid.