Lead AI Orchestration Engineer

Interflex Datensysteme GesmbH; Carmel (IN); Hybrid; USD 154,000 - 205,000; Full time

Benefits offered by this job

Health, dental and vision insurance

Unlimited PTO

401K with company match

Tuition Reimbursement

Wellness program

Job summary

Allegion is seeking a Lead Software Engineer, AI Agents & Orchestration, to design and operate scalable AI orchestration frameworks across software engineering. You will work at the intersection of software engineering, AI, data engineering, and governance to deliver auditable, secure business outcomes.

You will architect AI workflows, deploy MLOps/LLMOps, and enable enterprise AI governance while collaborating with cross-functional teams in a hybrid work model.

Qualifications

• Bachelor’s degree or equivalent practical experience in CS/AI related field.
• 5-7 years of experience in Data/AI/Software engineering or related.
• Experience with large-scale data pipelines and distributed processing.
• Experience deploying RESTful APIs and web services at scale.

Responsibilities

• Own the technical vision and architecture for AI workload orchestration platforms.
• Develop agent-to-agent and human-in-the-loop workflows for automated processes.
• Lead AI context engineering and multi-agent architectures.
• Build reusable orchestration patterns for rapid deployment of AI-enabled capabilities.
• Design scalable data pipelines and enterprise data products for AI use cases.
• Implement MLOps, LLMOps, governance, and observability.

Skills

Python

SQL

APIs

RESTful APIs

Cloud-native architecture

Education

Bachelor's degree in CS or related field

Tools

Databricks

Snowflake

Spark

Kafka

Airflow

Job description

Allegion is seeking a Lead Software Engineer, AI Agents & Orchestration, to design and operate scalable AI orchestration frameworks across software engineering. You will work at the intersection of software engineering, AI, data engineering, and governance to deliver auditable, secure business outcomes.

You will architect AI workflows, deploy MLOps/LLMOps, and enable enterprise AI governance while collaborating with cross-functional teams in a hybrid work model.