Bizmoni Corp.
AI Engineer
Founding-style AI engineer building Bizmoni's LLM infrastructure from scratch — model serving, RAG pipelines, agentic workflows and MLOps — for its AI Business Network platform. Hands-on Python/LLM stack (vLLM, vector DBs, Docker/Kubernetes, cloud). Part-time (8–16 hrs/week), equity-only compensation.
- Compensation
- Not disclosed
- Location
- Mexico City, CDMX, Mexico
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5+ years of daily production Python (async, typing, packaging, pytest, performance profiling)
- 2+ years shipping LLM-powered products, including one platform or major system built from scratch
- Strong experience with Docker, Kubernetes and at least one major cloud (AWS, Azure or GCP), plus infrastructure-as-code
- Practical knowledge of inference servers, GPU utilisation, batching, quantisation and caching
- Hands-on with embeddings and vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar) and reliable data pipelines
- Solid understanding of transformers, tokenisation and fine-tuning, with experience using PyTorch and Hugging Face
- CI/CD, observability, API design and maintainable software practices
- Comfortable making architectural calls with incomplete information and explaining trade-offs to non-technical stakeholders
- Personal computer with camera and reliable internet connection
- Willingness to participate in equity-based compensation model
Nice to have
- Experience as first or founding AI/ML engineer at a start-up or scale-up
- Agent frameworks and protocols such as LangGraph, LlamaIndex, DSPy or MCP
- Distributed training or multi-GPU work (DeepSpeed, FSDP, Ray)
- Experience in a regulated sector (fintech, healthcare, public sector) and familiarity with the EU AI Act
- Some Rust, Go or TypeScript alongside Python
- Open-source contributions, published projects or technical writing in the LLM space
Full description
About Bizmoni Corp.
Bizmoni is building an AI Business Network designed to help people and businesses connect, earn, learn, and grow in the AI era.
As a seed-stage, pre-revenue company, we are strengthening our platform, growing our global ecosystem, and laying the foundations for future funding and scale. This is an opportunity to join an entrepreneurial, international team and help shape both the product and the business.
About the Role
Architecture and platform build
- Design and build our LLM infrastructure from scratch: model serving, orchestration, data pipelines, vector storage and observability.
- Make and document key build-vs-buy decisions, including hosted APIs (OpenAI, Anthropic, Google) versus self-hosted open-weight models (Llama, Mistral, Qwen).
- Stand up scalable, cost-efficient inference using tools such as vLLM, TGI or Triton, on GPU and CPU infrastructure.
- Build the cloud foundations on [AWS / Azure / GCP] using infrastructure-as-code (Terraform or similar), Docker and Kubernetes.
LLM application engineering
- Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, hybrid search and re-ranking.
- Develop agentic workflows, tool use and function calling for production use cases.
- Fine-tune and adapt models where it adds value (LoRA/QLoRA, PEFT), and know when prompting or retrieval is the better answer.
- Expose AI capabilities through well-designed Python APIs (FastAPI or similar) for product and engineering teams.
Quality, reliability and safety
- Build evaluation frameworks to measure accuracy, hallucination, latency and cost, and use them to gate releases.
- Implement monitoring, tracing and alerting for LLM services (e.g. LangSmith, Langfuse, OpenTelemetry, Prometheus/Grafana).
- Put guardrails in place for data privacy, prompt injection, PII handling and GDPR compliance.
- Own CI/CD and MLOps practices for models, prompts and pipelines.
Leadership and collaboration
- Set coding standards, write clear technical documentation and review code.
- Partner with product, data and engineering leads to turn business problems into shipped AI features.
- Help shape future AI hiring and mentor engineers as the team grows.
What you'll bring
- Expert, hands-on Python. 5+ years writing production Python daily: async, typing, packaging, testing (pytest) and performance profiling. You write the code, not just the design doc.
- Built LLM systems in production. At least 2 years shipping LLM-powered products to real users, including at least one platform or major system you built from scratch.