At Tribe, we’re on a mission to help enterprises realize the value of AI for their business. Every large enterprise wants to use AI to transform how they operate — but many don’t have the capabilities to do it. That gap is our opportunity.
We’re an AI-native services company that helps enterprises build and deploy best-in-class AI products that deliver real business impact. We partner closely with OpenAI and Anthropic, giving us rare visibility into the most advanced models, roadmaps, and GTM strategies in the world.
We’re looking for a Forward Deployed AI Engineer to lead our most mission-critical client engagements. This is not a lab role - you’ll be embedded with enterprise clients, building and deploying AI solutions where the problems are messy, the stakes are high, and the environment is rarely “ideal.”
You’ll be the one who makes things work in the real world: taking designs from architects, wrestling them into production, and ensuring they scale reliably under real constraints. If you thrive on chaos, love solving problems end-to-end, and want your code to matter outside of demos, this role is for you.
Client Engagement & Delivery
Work side by side with clients, PMs, and Architects to scope and deploy AI systems that actually solve problems.
Build and integrate systems using LLMs, RAG pipelines, agent frameworks, vector DBs, and related tools.
Navigate real-world challenges: data access, authentication, security, networking, and brittle enterprise environments.
Solution Implementation
Translate high-level designs into working components that deliver measurable outcomes.
Debug relentlessly - optimize for reliability in production, not just elegance in code.
Write modular, reusable code that balances speed of delivery with long-term maintainability.
Collaboration & Enablement
Embed with client engineering teams to accelerate adoption and integration.
Share insights from field implementations to inform reusable components in our platform.
Document decisions with clarity so both clients and internal teams can build on your work.
5+ years of engineering experience, with proven work shipping AI/ML systems into production.
Strong Python skills and deep familiarity with modern AI/ML frameworks (LangChain, LlamaIndex, Hugging Face, vector DBs).
Hands-on with cloud deployment, orchestration, CI/CD, and distributed environments (AWS, Azure, GCP).
Ability to debug gnarly real-world issues (auth failures, networking black holes, flaky data pipelines).
Problem-solver who thrives in ambiguity, focused on delivery and reliability over process and ceremony.
Comfortable in client-facing environments - credible with engineers, clear with executives.
Impact: Ship AI systems that don’t just demo well but run at scale in Fortune 500 enterprises.
Growth: Stay hands-on with cutting-edge frameworks while developing field-tested instincts.
Variety: Solve problems across industries, from finance to healthcare to defense.
Culture: Work in a team that prizes resilience, creativity, and winning over process.
Trajectory: Build both your technical and consulting muscles in one of the most demanding roles in AI delivery.
As a Senior ML Engineer, you will develop scalable backend systems and APIs, operationalize ML models, and integrate advanced AI technologies to enhance decision-making for businesses.
Designs and deploys production-grade generative AI systems, including LLM applications, RAG pipelines, and agentic workflows, while improving reliability, scalability, safety, and business impact.
Own and deliver production-grade Generative AI and agentic solutions, from technical discovery through continuous improvement, applying strong software engineering, RAG, enterprise integrations, evaluation, and observability.
Develop AI agents, orchestration, and guided workflows for an enterprise AI platform, building conversational experiences and integrating agent capabilities with backend systems and observability tools.
Develop AI agents, orchestration, and conversational workflows for an enterprise AI platform, integrating Python, LangGraph, and LangChain with backend systems and observability tools.
