Why This Job is Featured on The SaaS Jobs
AI Engineer roles in SaaS increasingly sit at the intersection of product delivery and model operations, and this listing is notable for that applied emphasis. SINAI describes a platform that supports enterprise-grade reporting and modeling workflows, which typically demands reliability, auditability, and careful handling of complex customer data. Within the SaaS ecosystem, that combination makes LLM work less about demos and more about embedding AI into core workflows that customers depend on.
From a SaaS career perspective, the role offers durable experience in translating ambiguous product needs into production features—an enduring skill as AI becomes a standard layer across B2B software. The remit spans model evaluation, integration, deployment, and monitoring, which mirrors how many SaaS companies are structuring “full-lifecycle” applied AI. Collaboration with engineering and product also signals exposure to the cross-functional operating model common in modern SaaS teams.
This position fits a mid-level builder who prefers hands-on implementation over setting overarching AI direction. It should suit someone comfortable making pragmatic tradeoffs, iterating through experimentation, and owning production quality. Interest in LLM patterns such as RAG and operating cloud services would align well with the day-to-day shape of the work.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About SINAI
SINAI is a San Francisco–based climate technology company helping enterprises measure, analyze, and reduce carbon emissions. Our platform supports complex reporting, modeling, and regulatory workflows that enable companies to meet ambitious decarbonization targets.
We value ownership, collaboration, and pragmatic execution. We look for people who enjoy solving real-world problems, making thoughtful tradeoffs, and shipping reliable software in a fast-moving environment.
The Role
We’re hiring a mid-level AI Engineer to help build and integrate AI-powered features into our platform, with a strong focus on Large Language Models (LLMs).
In this role, you’ll work closely with Software Engineers, Product Managers, and Data-focused teammates to design, implement, and operate AI-driven capabilities. You won’t be defining company-wide AI strategy, but you will have meaningful ownership over implementation details, experimentation, and production quality.
This is a hands-on role focused on turning AI capabilities into reliable, customer-facing features.
What You’ll Do
- Design, build, and integrate LLM-powered features into SINAI’s platform
- Implement and iterate on AI-driven workflows that improve user experience and automate processes
- Evaluate and compare AI models to determine fit for specific product use cases
- Help deploy, monitor, and operate AI/ML features in production environments
- Collaborate with Engineering and Product to translate requirements into working AI solutions
- Contribute to experimentation, prototyping, and incremental improvement of AI capabilities
- Stay current with new tools, models, and best practices in applied AI
Required Qualifications
- 3+ years of professional software engineering experience, ideally in data-heavy or backend systems
- 1+ year of hands-on experience integrating LLMs into real applications (production or near-production)
- Experience working with modern AI models and APIs (e.g., OpenAI, Anthropic, Meta, or similar)
- Strong coding skills in Python or TypeScript/JavaScript
- Familiarity with AI/ML libraries or frameworks (e.g., LangChain, Hugging Face, PyTorch, TensorFlow)
- Experience deploying and supporting services in a cloud environment (AWS, GCP, or Azure)
- Solid problem-solving skills and ability to work effectively with partial requirements
- Professional proficiency in written and spoken English
Nice to Have
- Experience integrating OpenAI or similar APIs in a production environment
- Familiarity with vector databases and retrieval-augmented generation (RAG) patterns
- Exposure to AWS AI/ML services (e.g., Bedrock, SageMaker)
- Experience improving user workflows using AI or automation
- Experience working in SaaS products or startup environments
- Interest in climate, sustainability, or data-intensive domains
