Founding AI Engineer (B2B SaaS Funded Startup - Remote Work)

This is a deep-tech, hands-on AI engineering role focused on building production-grade AI systems, not demos. You will work on: • RAG pipelines • Multi-agent architectures • LLM orchestration layers • Real-time AI workflows This role requires someone who has built and shipped AI systems at scale, understands latency, evaluation, and reliability trade-offs, and can turn LLM capabilities into real business outcomes. What You’ll Build AI Agents for Recruiting • Design and build multi-agent systems that automate sourcing, screening, follow-ups, and candidate evaluation. • Develop agent orchestration frameworks for complex, multi-step workflows. • Build systems that can reason, act, and iterate autonomously. RAG & Knowledge Systems • Build and optimize RAG pipelines over structured + unstructured data (resumes,

s, conversations). • Work with vector databases, embeddings, and retrieval strategies (HNSW, hybrid search, reranking). • Improve grounding, reduce hallucinations, and enhance response quality. LLM Infrastructure & Performance • Optimize latency (TTFT), throughput, and cost for production systems. • Work on model optimization, quantization, caching, and batching strategies. • Build scalable inference systems using tools like vLLM, FastAPI, async pipelines. Evaluation, Observability & Feedback Loops • Design evaluation frameworks for retrieval + generation quality. • Build feedback loops and telemetry pipelines to continuously improve model performance. • Track metrics like accuracy, latency, hallucination rate, and user outcomes. Data & ML Pipelines • Build ETL and data pipelines for ingestion, processing, and feature generation. • Work with streaming systems (Kafka), batch systems, and real-time pipelines. • Enable continuous learning and improvement of AI systems. Collaboration & Ownership • Work closely with backend engineers to integrate AI systems into product workflows. • Take ownership of systems from design → build → deploy → scale. • Contribute to hiring, architecture decisions, and engineering culture. What We’re Looking For • 3–8 years of experience in ML/AI engineering or applied AI roles. • Strong hands-on experience with: • LLMs (GPT, Llama, etc.) • RAG architectures • Embeddings & vector databases • Experience building production-grade AI systems (not just prototypes). • Strong programming skills in Python. • Experience with FastAPI / Flask / async systems. • Understanding of latency optimization, scaling, and cost trade-offs. • Experience with data pipelines (PySpark, Airflow, etc.). • Strong problem-solving and system design skills.