AI/ML Engineer

About the Role

We're looking for a Junior AI/ML Engineer to help build AI-powered features into our SaaS platform. You'll work alongside senior engineers on LLM- and ML-driven capabilities, self-hosted model serving, retrieval pipelines, and agentic workflows, from prototype to production. This is a hands-on role with real ownership, ideal for someone early in their career who wants to grow fast in a founder-led environment.

What You'll Do

• Build and integrate LLM-powered features into production applications
• Work with self-hosted model serving (vLLM or similar) and OpenAI-compatible inference endpoints
• Build retrieval-augmented generation (RAG) pipelines, chunking, embeddings, vector search, reranking
• Develop and maintain data pipelines for ingestion, indexing, evaluation, and inference
• Build agentic, multi-step workflows using modern LLM orchestration frameworks
• Write clean Python for model serving, prompt orchestration, and backend integration
• Experiment with models, prompts, and eval strategies, then measure what actually works
• Help monitor, debug, and improve deployed AI systems

Must-Have Qualifications

• 1–2 years of experience, or strong projects/internships in AI/ML
• Solid Python fundamentals
• Understanding of core ML and LLM concepts (embeddings, inference, context windows, evaluation)
• Familiarity with LLM frameworks (LangChain, LlamaIndex, LangGraph, or similar)
• Basic understanding of RAG and vector databases
• Experience integrating models into applications via REST APIs
• Comfortable with Git and collaborative workflows
• Genuine curiosity and a fast, self-directed learning style

Nice to Have

• Hands-on with self-hosted inference (vLLM, TGI, Ollama, or similar)
• Experience with vector databases (pgvector, Qdrant, Weaviate, Pinecone, etc.)
• Exposure to agentic / multi-step AI workflows and tool-calling
• Familiarity with model fine-tuning, quantization, or GPU serving
• Cloud experience
• Awareness of sovereign/on-prem AI deployment considerations
• Any background in data engineering, MLOps, or cybersecurity
• A portfolio, GitHub, or Kaggle profile showing real work

Who You Are

• You learn fast and aren't afraid to ask questions
• You care about shipping working features, not just notebooks
• You're comfortable with ambiguity in an early-stage environment
• You take ownership of what you build

What We Offer

• Direct mentorship from senior engineers and the founders
• Real production ownership from early on
• A front-row seat building AI into a real product
• Competitive compensation for the level

Qualifications

Bachelor's degree in Computer Science, AI, Data Science, or a related field, or equivalent demonstrated project work. What you've built matters more than where you studied.