AI Engineer

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

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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.

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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.

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Infrastructure depth. Strong experience with Docker, Kubernetes and at least one major cloud (AWS, Azure or GCP), plus infrastructure-as-code.

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Model serving and optimisation. Practical knowledge of inference servers, GPU utilisation, batching, quantisation and caching to manage latency and cost.

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RAG and data. Hands-on with embeddings and vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar) and building reliable data pipelines.

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ML fundamentals. Solid understanding of transformers, tokenisation and fine-tuning, with experience using PyTorch and Hugging Face.

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Engineering discipline. CI/CD, observability, API design and writing software others can maintain.

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Ownership. Comfortable making architectural calls with incomplete information, explaining trade-offs to non-technical stakeholders, and working without a ready-made playbook.

Nice to have

• Experience as the first or founding AI/ML engineer at a start-up or scale-up.

• Agent frameworks and protocols such as LangGraph, LlamaIndex, DSPy or Model Context Protocol (MCP).

• Distributed training or multi-GPU work (DeepSpeed, FSDP, Ray).

• Working 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.

Who Thrives at Bizmoni?

We look for people who demonstrate:

• Ownership – Taking responsibility for outcomes, not just tasks

• Results Orientation – Focusing on quality, execution, and impact

• Continuous Improvement – Always looking for better ways to work and improve quality

• Collaboration – Partnering effectively across teams and functions

• Adaptability – Thriving in a dynamic and evolving environment

• Mission Alignment – Believing in what we're building and its future potential

Compensation & Commitment

Bizmoni currently offers ONLY an equity-based compensation model, aligned with global startup practices that enable early contributors to participate in the company's potential long-term value creation.

The role requires a flexible commitment of approximately 8–16 hours per week, with clear deliverables and results-driven accountability. Contributors may maintain a primary paid role alongside their engagement with Bizmoni. As Bizmoni is currently seed-stage, compensation is provided in company equity. Full terms and conditions will be discussed during the selection process and confirmed in the consulting agreement.

Requirements for Remote Engagement:

• Personal computer with camera and reliable internet connection

• Willingness to participate in Bizmoni's equity-based compensation model

Why You'll Love Working at Bizmoni

• Design the core AI agents of an emerging AI Business Network from day one

Originally posted on Himalayas