AI Software Engineer

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 AWS experience, 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.