AI Engineer II – GenAI & LLMs

Who we are:

Zinier’s No-Code Customization field service automation platform empowers field service organizations with the combined power of humans and technology to keep our world up and running. 

No two field service organizations are alike…

From the IT ecosystem you connect with, to the workflows executed in the field, your business requirements are unique. And in the fast-paced world of field service, those requirements can change rapidly. And that’s exactly why you need Zinier.

ZiniApps solve challenges across the entire lifecycle of field work. Off-the-shelf functional, but also customizable to meet specific use cases, ZiniApps cover the most meaningful jobs to be done in field service installation and maintenance.

We are a global team headquartered in Silicon Valley with leading investors including Wipro, Telmex, Black & Veatch, NCR, Toshiba and Qualcomm Ventures LLC.

To learn more, check out www.zinier.com

Why we exist:

Services shape how we live. Electricity lights up our homes. The Internet opens up our worlds. Cellular phones keep us connected no matter where we are. We take for granted the things we can turn on with the flip of a switch. But when even one of the services we depend on isn’t available, the day can quickly start to go sideways.

For organizations that provide these services, some of the most important work happens in the field — in neighborhoods, across open spaces, and along millions of last miles that criss-cross the country. Every moment of downtime matters, which is why Zinier exists. Zinier empowers organizations to work smarter — from the main office to the field — to solve problems quickly, fix things before they break, and keep people in the rhythm of their days.

To do this, Zinier has created a scalable platform powered by AI-driven insights and intelligent automation that helps field service teams work smarter, better, faster, and more efficiently. We help organizations automate routine tasks so the people in the field can focus on putting their expertise to work. We work with customers in telecom and energy.

What we’re looking for:

Join a lean, high-velocity AI team building GenAI infrastructure that powers enterprise workflows at scale. You'll ship production-grade LLM solutions, run experiments, and work directly with the Lead Architect – AI

What You'll Build:
LLM-powered solutions – Design and deploy models that solve real enterprise workflow challenges
Multi-provider integrations – Work with OpenAI, Claude, and open-source models (LLaMA, Mistral, Falcon)
Advanced prompting & RAG pipelines – Implement prompt engineering, context management, and retrieval-augmented generation
Fine-tuning & evaluation loops – Curate datasets, tune models, and build reproducible evaluation frameworks
Production ML infrastructure – Build Python pipelines using Hugging Face (Transformers, PEFT, Datasets), LangChain, and LlamaIndex
Performance monitoring – Define metrics, track model drift, and maintain dashboards that ensure reliability

Roles & Responsibilities: 
3-5 Years Experience with Strong Python fluency for model training, evaluation, and API integration
CV & NLP fundamentals – experience with image processing, text parsing, and embedding generation
LLM expertise – hands-on with fine-tuning, prompt engineering, and context optimization
Modern tooling – comfortable with Hugging Face ecosystem, LangChain/LlamaIndex, and vector databases (FAISS, Pinecone, Weaviate)
API integration experience – worked with LLM APIs (OpenAI, Claude, Cohere) and deployed small models to production
Rigorous experimentation – dataset curation, benchmark design, and systematic eval loops
Enterprise awareness – understands security, versioning, and scalability requirements

Bonus Points:
Backend development exposure (FastAPI, Node.js) for seamless model-service integration
Containerization & GPU infrastructure (Docker, Kubernetes)
AWS AI services (SageMaker, Lambda, ECS/EKS)
MLOps practices (CI/CD pipelines, model monitoring, version control)

Why This Role:
Solve real problems – Start with workflow friction, not models. Build GenAI for global field
operations.
Ship fast – Prototype, measure, iterate. Deploy production code that scales.
Build the category – Define how AI transforms field service. Startup velocity, enterprise reach.