Job description

ABOUT US Asteri is an AI-native Work Intelligence and Orchestration Platform that gives large enterprises the intelligence to understand how work is performed today and how it should be performed in the era of AI, eliminating inefficiencies and identifying high-impact opportunities for AI augmentation. Our platform orchestrates AI and human work with enterprise-grade governance, auditability, and measurement built for production environments at scale. THE ROLE We are looking for an AI & ML Engineer with strong software engineering foundations to join our growing engineering team. This role sits at the intersection of applied AI and production software and focuses on building reliable, scalable, enterprise-ready AI systems. You will work closely with software engineers and platform teams to design, deploy, and operate AI-powered capabilities that directly power our core product. This is not a research-only role: success is measured by production impact, reliability, and customer value. If you are excited about building AI systems that operate in real enterprise environments with real constraints, real users, and real accountability - we’d love to talk. WHAT YOU’LL DO - Design, build, and operate production-grade AI/ML systems that power Asteri’s orchestration platform - Collaborate closely with backend, platform, and frontend engineers to integrate AI capabilities into scalable, reliable product workflows - Deploy and iterate on LLM-based applications in production, continuously evaluating quality, latency, and cost - Own retrieval and agentic systems (e.g., RAG pipelines, workflow agents, policy-driven logic) end-to-end - Define and run rigorous evaluation and testing for AI systems, including offline experiments and production monitoring - Improve model performance and system behavior over time through experimentation, tuning, and system-level optimizations - Implement strong engineering practices for AI development, including testing, CI/CD, versioning, and rollback strategies - Stay current with advances in applied AI and generative models and translate relevant techniques into practical product improvements - Partner with cross-functional teams to understand product requirements and translate them into robust AI solutions WHAT WILL MAKE YOU SUCCESSFUL MUST-HAVES - Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience - Strong proficiency in Python and experience building production software - 5+ years of professional experience spanning software engineering and applied AI/ML development - Experience deploying and operating ML or LLM-based systems in production environments - Solid understanding of machine learning fundamentals, model evaluation, and system-level tradeoffs - Experience with cloud-based deployment of ML systems - Strong software engineering skills, including systems design, testing, and performance optimization - Ability to reason about and communicate complex technical concepts clearly - Comfortable working independently while collaborating closely with a senior engineering team NICE-TO-HAVES - Experience building applications with LLM frameworks such as LangChain, LlamaIndex, or Hugging Face - Deep experience with retrieval-augmented generation (RAG) systems and optimization techniques - Familiarity with ML lifecycle and experimentation tools (e.g., MLflow, Weights & Biases, DVC) - Experience building distributed or data-intensive systems - Exposure to enterprise or regulated environments WHY JOIN US You’ll have direct ownership of critical AI systems, real production impact, and the opportunity to shape how AI is safely and effectively deployed alongside humans at scale. Competitive compensation with remote-friendly culture, and a pragmatic, production-first approach to AI.

Requirements

Must-haves

If you are excited about building AI systems that operate in real enterprise environments with real constraints, real users, and real accountability - we’d love to talk.
Design, build, and operate production-grade AI/ML systems that power Asteri’s orchestration platform
Collaborate closely with backend, platform, and frontend engineers to integrate AI capabilities into scalable, reliable product workflows
Deploy and iterate on LLM-based applications in production, continuously evaluating quality, latency, and cost
Own retrieval and agentic systems (e.g., RAG pipelines, workflow agents, policy-driven logic) end-to-end
Define and run rigorous evaluation and testing for AI systems, including offline experiments and production monitoring
Improve model performance and system behavior over time through experimentation, tuning, and system-level optimizations
Implement strong engineering practices for AI development, including testing, CI/CD, versioning, and rollback strategies
Stay current with advances in applied AI and generative models and translate relevant techniques into practical product improvements
Partner with cross-functional teams to understand product requirements and translate them into robust AI solutions
Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience
Strong proficiency in Python and experience building production software
5+ years of professional experience spanning software engineering and applied AI/ML development
Experience deploying and operating ML or LLM-based systems in production environments
Solid understanding of machine learning fundamentals, model evaluation, and system-level tradeoffs
Experience with cloud-based deployment of ML systems
Strong software engineering skills, including systems design, testing, and performance optimization
Ability to reason about and communicate complex technical concepts clearly
Comfortable working independently while collaborating closely with a senior engineering team

Nice-to-haves

Experience building applications with LLM frameworks such as LangChain, LlamaIndex, or Hugging Face
Deep experience with retrieval-augmented generation (RAG) systems and optimization techniques
Familiarity with ML lifecycle and experimentation tools (e.g., MLflow, Weights & Biases, DVC)
Experience building distributed or data-intensive systems
Exposure to enterprise or regulated environments

Application prep

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Keywords to mirror

aimlllmci/cdpythonmachine learning

Platform: ashby
Effort: quick