As an AI Engineer you will help build Trustible's Governance Platform and AI capabilities. You will contribute to building AI agents and tooling that help organizations accelerate their AI governance operations, identify and mitigate risks of AI, and prepare for AI audits and assessments. You will help design and implement MCP (Model Context Protocol) servers, build and test AI agents, and develop evaluation workflows to assess AI systems and their outputs. You will work closely with senior engineers to ensure our AI systems are reliable, well-tested, and scalable across cloud and on-prem/hybrid deployments, and integrate with existing MLOps platforms and GRC reporting tools.
Compensation Range: $110,00-130,000
What you'll do
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
Qualifications
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
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
About You
You understand the amazing power of Artificial Intelligence to solve complex problems and make a positive impact in the world, but you also understand the risks it brings to perpetuate existing issues, mislead or manipulate people, and cause physical, emotional or reputational harm to people. You want to devote your time to help make a positive impact in the world and work with organizations of all sizes, across all industries, to improve their own internal operations. You are excited at the idea of growing your skillset, working outside your comfort zones and always putting your customers first. You are excited to work at an early stage startup and are ready to navigate some of the natural uncertainty that comes with that. You value getting to know your colleagues and spending time together as a team and helping build a positive, inclusive working environment.
Why Join Trustible?
You’ll join a super smart, agile team at the intersection of AI and policy supporting one of the fastest-growing segments within the AI ecosystem (AI Governance)
Work alongside an experienced founding team
Competitive salary, equity options, and benefits package.
Hybrid work environment (in-office 2-3 times a week, WFH Mondays and Fridays)