AI Software Engineer

VSG Business Solutions LLC; Farmington Hills (MI); On-site; USD 150,000 - 190,000; Full time

Job summary

VSG Business Solutions LLC is seeking an AI Software Engineer III to join an agentic engineering team building production-grade services and platform components. The role emphasizes AI safety, guardrails, and distributed-system design with strong Python and TypeScript coding, AWS, and Terraform-based infrastructure.

The engineer will lead hands-on development, define interfaces, and mentor peers while addressing prompt-injection and other AI risks in a large-scale environment.

Qualifications

• Senior software engineer role focused on production-grade backend services.
• Strong experience in Python and TypeScript for backend and platform work.
• Hands-on expertise with AWS, Terraform, and IaC practices.
• Experience integrating LLMs and building agentic workflows with safety controls.

Responsibilities

• Design and implement guardrails, validation, filtering, and enforcement components.
• Architect scalable backend services and APIs across distributed environments.
• Lead agentic AI workflows and integrate LLMs with business systems.
• Apply defensive programming and observability to ensure reliability.

Skills

Python

TypeScript

Guardrails

Input validation

Output filtering

AWS

Terraform

LLM integration

Distributed systems

APIs

Mentoring

Secure coding

Tools

AWS

Lambda

Fargate/ECS

API Gateway

CloudWatch

Job description

Title:- AI Software Engineer

Position Overview

We are seeking an AI Software Engineer III to join an AI agentic engineering team responsible for building production-grade services, controls, and platform components that govern how AI-enabled systems behave.

This is a hands-on senior software engineering role, not a traditional Data Science or Machine Learning research position. The successful candidate will combine strong Python and TypeScript engineering, production AWSexperience, and Terraform-based infrastructure engineering with an understanding of modern LLM and agentic application architectures.

A major focus of the role is AI safety and enforcement architecture. You will design and implement guardrails, filtering, validation, and other defensive controls that operate across multiple services. You will help determine where enforcement belongs within a distributed architecture, establish reusable patterns and interface contracts, and ensure those controls behave predictably under misuse, failure, malicious input, and unexpected system conditions.

You will also serve as a technical leader who remains close to the code while helping other engineers adopt safe, resilient, and repeatable engineering practices.

Core Responsibilities AI Guardrails & Enforcement Architecture

• Design and implement guardrail, validation, filtering, and enforcement components for AI-enabled applications.

• Determine where controls should be applied across service boundaries, including:

  • Input validation

  • Prompt-injection detection and mitigation

  • Model input controls

  • Tool-use authorization

  • Output validation and filtering

  • Policy enforcement

  • Failure and fallback handling

• Establish reusable enforcement patterns that can be adopted by multiple engineering teams.

• Design systems to behave safely when encountering malformed, malicious, unexpected, or adversarial inputs.

• Define clear boundaries between application logic, AI orchestration, model interaction, and safety controls.

Backend & Platform Engineering

• Design, develop, test, and maintain production-quality backend services and APIs using Python and TypeScript.

• Architect scalable services that operate across distributed application environments.

• Develop shared libraries, services, APIs, and platform components with broad organizational use.

• Define stable interfaces and service contracts consumed by other engineering teams.

• Apply defensive programming principles to distributed and AI-enabled applications.

• Design for service resiliency, fault isolation, graceful degradation, and predictable failure behavior.

Agentic AI Engineering

• Design and build agentic, multi-step, or multi-agent workflows.

• Integrate LLMs with backend systems, APIs, tools, data sources, and business services.

• Design appropriate controls around agent actions and tool invocation.

• Address AI-specific risks including:

  • Prompt injection

  • Untrusted model output

  • Hallucinated or malformed responses

  • Unauthorized tool use

  • Unexpected agent behavior

  • Cross-service failure propagation

• Build systems that treat LLM outputs as potentially untrusted inputs requiring appropriate validation.

AWS & Infrastructure Engineering

• Design and deploy production systems in AWS.

• Work with services such as:

  • AWS Lambda

  • ECS/Fargate

  • API Gateway

  • IAM

  • CloudWatch

  • Related serverless and container-based AWS services

• Build and maintain production infrastructure using Terraform.

• Apply Infrastructure-as-Code practices that support repeatable deployments, secure configurations, and scalable environments.

• Participate in architectural decisions involving compute, networking, IAM, service boundaries, and deployment patterns.

Observability & Reliability

• Instrument distributed systems using:

  • Structured logging

  • Metrics

  • Distributed tracing

  • Error monitoring

  • Service health indicators

• Improve visibility into behavior across service boundaries.

• Design systems that enable engineers to identify where and why enforcement or workflow failures occur.

• Consider downstream dependencies, timeouts, retries, partial failures, and failure propagation when designing services.

Technical Leadership

• Mentor junior and mid-level engineers on:

  • Defensive programming

  • Safe AI integration

  • API and service design

  • Error and exception handling

  • Failure management

  • Secure coding practices

• Define technical patterns and engineering standards that other developers can consistently follow.

• Communicate architectural decisions clearly through documentation, diagrams, code reviews, and technical discussions.

• Develop experience-backed technical opinions and constructively challenge designs when appropriate.

• Remain a hands-on engineer capable of implementing the systems and patterns being recommended.

Required Qualifications

• 5 8 years of professional software engineering experience.

• Strong production software development experience using Python.

• Strong production software development experience using TypeScript.

• Demonstrated experience designing and building backend services and APIs.

• Strong experience delivering production systems in AWS.

• Hands-on experience with AWS services such as Lambda, Fargate/ECS, and API Gateway.

• Strong production experience with Terraform and Infrastructure as Code.

• Experience designing systems that operate across multiple services or distributed system boundaries.

• Experience defining interfaces, contracts, reusable components, or engineering patterns used by other development teams.

• Experience with LLM integrations or GenAI-enabled applications.

• Familiarity with AI safety and LLM integration concepts such as:

  • Prompt-injection detection

  • Guardrail design

  • Input validation

  • Output filtering

  • Policy enforcement

• Experience designing or building agentic workflows, multi-step AI workflows, or multi-agent systems.

• Understanding of distributed-system concerns including dependencies, failure propagation, retries, resiliency, and contract stability.

• Ability to mentor engineers and communicate defensive software engineering practices.

• Strong written and verbal technical communication skills.

Preferred Qualifications

• Hands-on experience with AWS Bedrock.

• Experience invoking and integrating foundation models through Bedrock.

• Experience with AWS Bedrock Guardrails.

• Exposure to Amazon Bedrock AgentCore or comparable agent runtime technologies.

• Experience building developer platforms, shared engineering services, or internal developer tooling.

• Experience developing centralized policy or enforcement services.

• Experience implementing distributed tracing and advanced production observability.

• Experience with secure software development or application security principles.

• Experience designing authorization or policy controls around AI agent tool usage.