AI/ML Engineer - Product & Deployment

About the Role

We need an engineer who can take AI capabilities and turn them into reliable, production-grade products. This is a full-loop role: you own the work from prototype to backend to deployment to production monitoring to iteration.

You will be working on the real Dhron stack FastAPI backends, WebSocket real-time pipelines, Redis event streams, Docker sandbox execution environments, LLM integrations (Gemini, OpenAI, Claude), voice pipelines with STT/TTS, and vision inference systems. Every piece of what you build reaches paying customers.

What You'll Own

• Building and maintaining backend services and APIs using Python and FastAPI
• Integrating LLMs, embeddings, vector databases, RAG pipelines, and agent architectures into shipping products
• Designing databases, authentication flows, third-party integrations, and internal APIs
• Containerizing applications with Docker and deploying on GCP
• Building and maintaining CI/CD pipelines for staging and production
• Setting up logging, monitoring, alerts, and observability across the stack
• Debugging across every layer — application, API, model, database, cloud, network
• Optimizing systems for reliability, latency, and cost
• Turning working prototypes into products that don't break in front of customers
• Taking features end-to-end: build, deploy, monitor, iterate

Must Have

• Strong Python and backend development experience
• Hands-on experience building and deploying REST APIs (FastAPI, Flask, or similar)
• Practical Docker experience — you've containerized and deployed real applications
• Cloud deployment experience on GCP, AWS, or Azure
• CI/CD and Git/GitHub workflows
• Working knowledge of databases (SQL and NoSQL), authentication, and API design
• Experience integrating LLMs or AI APIs into real applications
• Strong debugging skills — you can follow a problem across layers to root cause
• Ownership mindset — you don't hand off broken things

Good to Have

• FastAPI, PostgreSQL, Redis in production
• Kubernetes / GKE, Terraform, GitHub Actions
• Vector databases (Pinecone, Weaviate, pgvector, Qdrant)
• RAG systems, LangChain / LangGraph, or agent frameworks
• Real-time systems experience (WebSockets, streaming)
• Voice AI (STT, TTS, telephony) or computer vision inference
• Experience taking an AI prototype from Jupyter notebook to production

Who This Role Is For

Someone who wants scope, not a narrow slot. You will not be handed a ticket queue. You will be given problems and expected to figure out the right way to solve them, ship the solution, and improve it based on what production tells you.

Fundamentals, ownership, and speed of learning matter more than knowing every technology on the list. If you can build it, deploy it, debug it, and make it better — we want to talk.

Growth Path

We hire people we intend to grow with. This role can expand into technical leadership as the platform scales, with scope over architecture, hiring, and product direction for engineers who want that trajectory.