What are the responsibilities and job description for the AI Engineer position at Veridic Solutions?
Job Summary:
As a Software Engineer on the Agentic System Layer (ASL) team, you will build and maintain the core platform components that power ***' agentic AI systems. Day-to-day responsibilities include: developing and maintaining agent orchestration services, tool registries, and execution runtimes; building APIs and microservices for LLM integration, prompt management, and agent lifecycle management; implementing observability, logging, and monitoring for agentic workflows; writing comprehensive tests (unit, integration, end-to-end) for platform reliability; collaborating with architects on design decisions and with ML engineers on model integration; participating in on-call rotations for production support of the AI platform; contributing to CI/CD pipelines, infrastructure-as-code, and deployment automation.
Top 3 Mandatory Skills and Experience:
1) 5 years software engineering experience with strong proficiency in Python, plus working knowledge of at least one of Java, Go, or TypeScript; hands-on experience building production REST/gRPC APIs and microservices.
2) Solid experience with cloud platforms (AWS or Azure preferred), containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code (Terraform, CloudFormation, or Pulumi).
3) Working experience with LLM integration patterns, prompt engineering, or AI/ML application development; familiarity with frameworks like LangChain, LangGraph, or similar orchestration tools.
Nice to Have Skills:
Experience with event-driven architectures (Kafka, EventBridge), vector databases, observability tools (Datadog, Splunk, OpenTelemetry), agent evaluation frameworks, FastAPI/Flask, async Python, Redis/caching patterns, airline or travel industry experience.
What Makes a Great Candidate?:
A great candidate is a solid mid-level to senior engineer who writes clean, testable code and has genuine curiosity about AI/ML systems. They do not need to be a deep ML expert, but they should understand how LLMs work, what agentic patterns look like, and how to build reliable services around non-deterministic components. They take ownership, write good tests, communicate clearly in code reviews, and are comfortable operating production systems.