- Company Name
- Raynmaker
- Job Title
- Artificial Intelligence Engineer
- Job Description
-
**Job Title**
Artificial Intelligence Engineer
**Role Summary**
Senior AI/ML Engineer responsible for designing, building, and production‑scaling core intelligence for an agentic AI platform. The role spans end‑to‑end ML engineering, LLM integration, retrieval‑augmented generation (RAG), reinforcement learning, and multi‑tenant infrastructure.
**Expectations**
- Own architecture, performance, cost, and reliability of AI systems.
- Deliver production‑grade pipelines and services at scale.
- Collaborate cross‑functionally with engineering leadership to shorten the whiteboard‑to‑deployment cycle.
- Drive continuous optimization of latency, memory, and token usage.
**Key Responsibilities**
- Design and optimize RAG systems using Milvus, Weaviate, Pinecone, or Zilliz.
- Build ranking, scoring, and routing models for live inference.
- Develop custom LLM deployments: fine‑tuning, inference routing, token optimization.
- Architect tool‑calling flows, agent memory, and multi‑turn reasoning.
- Implement reinforcement learning pipelines to evolve agent behavior.
- Create streaming inference pipelines for voice, chat, and WebSocket interactions.
- Build multi‑tenant ML infrastructure with data isolation, observability, and scale.
- Develop and maintain ML APIs and microservices in Docker/Kubernetes.
- Deploy, monitor, and troubleshoot real‑time and batch data pipelines.
- Own the ML model lifecycle: development, evaluation, deployment, tuning.
- Ensure production reliability, monitoring, and cost efficiency.
- Collaborate on platform‑wide architecture and data contracts.
**Required Skills**
- 7+ years of experience in ML, AI, or data engineering.
- Expert Python for backend, ML workflows, and orchestration.
- Deep knowledge of vector databases, retrieval systems, and RAG.
- Experience with modern LLM frameworks (LangChain, LangGraph).
- Production experience with reinforcement learning.
- Strong background in distributed systems, Docker, and Kubernetes.
- Proven ability to build and maintain streaming or real‑time pipelines.
- Familiarity with ML model lifecycle management and performance optimization.
- Optional: AWS ML stack (SageMaker, Bedrock), Kafka/Kinesis/Pulsar, model compression/quantization, CRM/sales tech expertise.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
- Advanced certifications in ML or AI (e.g., TensorFlow, PyTorch, AWS ML, Google Cloud AI) are a plus.