AI Engineer (Mid-Level)

Clera connects exceptional startup talent with early-stage companies building AI-native products, scalable SaaS, and consumer apps. We match engineers with roles where they ship quickly, influence direction, and work directly with founders and product teams.

What you will do

• Design, build, and maintain agentic systems that automate complex, multi-step workflows across domains such as healthcare, legal, fintech, logistics, and compliance.
• Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure (vector DBs, embeddings, indexing) for domain-specific search at scale.
• Implement multi-agent orchestration, tool-calling, memory, and reasoning components to deliver robust AI-driven user experiences.
• Develop evaluation and safety infrastructure to measure model performance, surface regressions, and enforce enterprise-level trust and reliability.
• Ship full-stack AI products from MVP to enterprise-grade: design APIs and data models, implement frontend and backend code, and operate production systems (CI/CD, monitoring, testing).
• Collaborate with founders, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry.

Minimum qualifications

• 2–8 years of software engineering experience with demonstrated delivery of shipped user-facing or backend products.
• Practical experience deploying LLMs/LLM-based services in production (engineering role), including prompt design, orchestration, and tool integration.
• Proficiency across the stack: Python plus TypeScript/React or equivalent; experience with cloud platforms (AWS or GCP) and relational or NoSQL databases.
• Working knowledge of RAG patterns, vector databases, embeddings, and retrieval pipelines, and the judgement to choose appropriate approaches.
• Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demos.

Preferred qualifications

• Experience with agent or workflow frameworks (e.g., LangGraph, CrewAI) and orchestration tools (e.g., Temporal, Trigger).
• Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration.
• Background building multi-tenant or enterprise-ready systems, or working in regulated industries (healthcare, fintech, legal).
• Experience designing API-driven, high-throughput systems and real-time product features.

What successful candidates value

• Ownership: delivering end-to-end features from data model to deploy and monitoring.
• User-centric engineering: prioritizing maintainable code that solves measurable user problems.
• Pragmatic architecture: making tradeoffs appropriate for early-stage growth and measurable impact.
• Continuous learning: adopting tools and practices that accelerate development while maintaining reliability and safety.

Interview process (typical)

• Screening conversation to assess fit, communication, and genuine curiosity about AI.
• Technical deep-dive on architecture and system design for real-world AI problems.
• Coding or pairing session to evaluate practical engineering and product delivery skills.

About Clera

Clera represents engineers with deep AI experience to early-stage startups backed by leading investors. We focus on matching candidates with roles that fit their product and career goals, ensuring companies request interviews and candidates stay in control. Our team provides responsive, candidate-first support throughout the hiring process.