AI Engineer

Trustible is a fast-growing, venture-backed startup focused on enabling responsible AI governance for Fortune 500 companies and technology firms. As an AI Engineer, you will contribute to building Trustible's Governance Platform, developing AI agents, and ensuring the reliability and scalability of AI systems.

Responsibilities

• You will be an individual contributor helping implement Python-based MCP servers, tools, and AI agents that help companies adopt trustworthy and responsible AI practices
• You will help define and follow a software development lifecycle (SDLC) process that includes weekly deployments and automated tests on top of a containerized runtime
• You will help translate regulatory and governance requirements for AI into evaluation pipelines, data models, and configurations for AI systems
• You will build, test, and iterate on AI agents (e.g., LLM-based workflows) including prompt design, tool integration, and guardrails
• You will design and run experiments to evaluate AI systems and their results, including creating test cases, benchmarks, and metrics for quality, reliability, and risk
• You will write end-to-end automated unit tests and acceptance test criteria for AI components, MCP services, and related integrations
• You will help deploy, monitor, and maintain AI services and infrastructure in our cloud environment (e.g., AWS services such as EC2, ECS/EKS, S3, and RDS)
• You will help integrate a Python backend with machine learning tools, libraries, and platforms (e.g., LLM providers, vector databases, MLOps tools)
• You have excellent written and verbal communication skills

Skills

• 1–3 years of experience as a Software Engineer, Machine Learning Engineer, or AI Engineer (including internships, co-ops, or post-grad roles)
• 1–3 years of experience working with Python in a production or near-production environment
• Experience building or integrating web services or APIs (e.g., REST, FastAPI, Django, Flask, or similar)
• Familiarity with modern AI/ML tooling, such as large language models (LLMs), prompt engineering, RAG pipelines, or ML frameworks
• Experience working with at least one cloud provider (AWS preferred) and containerized applications (Docker; Kubernetes experience is a plus)
• Comfortable writing tests, debugging issues, and shipping code as part of a CI/CD workflow
• Excellent written and verbal communication skills
• Experience with or interest in building MLOps, governance, or GRC-related tools is a major plus
• Exposure to or interest in MCP (Model Context Protocol), AI agents, or tool-calling frameworks is a strong plus

Benefits

• Equity options
• Benefits package
• Hybrid work environment (in-office 2-3 times a week, WFH Mondays and Fridays)

Company Overview