Posted Jun 2, 2025
Develop and integrate LLM-powered features such as AI-assisted workflow automation, code generation, and content strategy. - Optimize AI performance through prompt engineering, retrieval-augmented generation (RAG), and evaluation frameworks. - Build and scale AI infrastructure, ensuring low-latency responses, caching, and cost-efficient model usage. - Implement AI observability and safeguards, monitoring quality, security, and compliance. - Collaborate with product and engineering teams to deliver intuitive, AI-driven user experiences. - Stay ahead of AI advancements, continuously improving our AI-powered capabilities. - Collaborate with early adopters to optimize model performance and usability. ## Qualifications
3+ years of experience in machine learning engineering, AI/LLM integration, or applied NLP
Proven track record of building LLM-powered applications
Strong experience with foundation models (GPT-4o, Claude, etc.) and advanced prompt engineering
Experience with embedding models (e.g., OpenAI Ada, Cohere, or local vector stores like pgvector, Weaviate, Pinecone)
Deep understanding of retrieval-augmented generation (RAG) and contextual AI response optimization
Familiarity with LangChain, or similar frameworks for orchestrating LLM-powered applications
Strong programming skills in Python (experience with AI frameworks like Hugging Face, LangChain, or OpenAI SDK)
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Experience in evaluating LLM performance, running A/B tests, and implementing feedback loops for AI refinement
Solid understanding of caching, rate limiting, and cost optimization strategies for AI workloads
Ability to work cross-functionally with engineers, product managers, and end users to develop impactful AI solutions
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