AI Engineer (AI-Native)
LLMS, LLC
United States
21 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
2 years minimum
Working hours
Regular working hours
Job source
Tech stack
A/B Testing
Artificial Intelligence
Computer Programming
Python (Programming Language)
Machine Learning
Open Source Technology
Search Technologies
Software Engineering
TypeScript
Large Language Models
Multi-Agent Systems
Model Validation
+3 more
Build Management
Kubernetes
Machine Learning Operations
Job description
We’re looking for a Senior AI Engineer who doesn’t just work with AI, they think in it. This is a role for someone who has internalized AI-first development patterns, builds with LLMs as a primary primitive, and can architect systems that put intelligent automation at the core rather than the edge.
What You’ll Do
- Design and build production-grade AI systems including LLM-powered pipelines, agentic workflows, and retrieval-augmented generation (RAG) architectures
- Lead the integration of AI capabilities across products, from prototyping through to scalable deployment
- Evaluate, fine-tune, and optimize foundation models for specific use cases; stay current on the rapidly evolving model landscape
- Define engineering best practices for prompt engineering, model evaluation, observability, and safety guardrails
- Collaborate with product and platform teams to identify high-leverage AI opportunities
- Mentor engineers on AI-native development patterns and help level up the broader team
Requirements
- 5+ years of software engineering experience, with at least 2 years focused on applied AI/ML systems
- Deep hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini, etc.) and orchestration frameworks such as LangChain, LlamaIndex, or similar
- Strong programming skills in Python, TypeScript, or equivalent, we care more about engineering fundamentals than language loyalty
- Experience with vector databases (Pinecone, Weaviate, pgvector), embeddings, and semantic search
- Proven ability to ship AI features to production, not just demos or notebooks
- Comfort operating in ambiguity: you can take a vague idea and turn it into a scoped, working system
- Experience with evaluation frameworks, A/B testing for model outputs, and monitoring for model drift or degradation
Nice to Have
- Experience with fine-tuning or RLHF workflows
- Familiarity with multi-agent architectures and tool-use patterns
- Background in ML engineering (training pipelines, model serving, MLOps)
- Contributions to open-source AI projects
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