Job Url: https://www.remoterocketship.com/company/invoca/jobs/senior-applied-ai-engineer-united-states-remote Job Description: Invoca Website LinkedIn All Job Openings Invoca is a leading provider of a revenue execution platform designed to help marketing, sales, and contact centers drive more revenue. They specialize in call tracking, artificial intelligence, and interaction management to optimize revenue growth. Their innovative platform connects marketing investments directly to offline sales through AI capabilities, offering detailed insights into buyer behavior from calls. Invoca supports a wide range of industries including automotive, financial services, healthcare, home services, insurance, retail, telecom, and travel & hospitality. The company is recognized for helping businesses optimize ad spend and improve campaign performance through full attribution for every call. Inbound Call Marketing β€’ Call Tracking β€’ Call Intelligence β€’ Pay-Per-Call Advertising β€’ Phone Leads 201 - 500 employees Founded 2012 πŸ€– Artificial Intelligence 🀝 B2B πŸ“‘ Telecommunications Senior Applied AI Engineer July 19 πŸ‡ΊπŸ‡Έ United States – Remote πŸ’΅ $160k - $252k / year ⏰ Full Time 🟠 Senior πŸ€– AI Engineer πŸ¦… H1B Visa Sponsor AWS Open Source Pandas Python PyTorch Go Apply Now Receive Emails with Similar Jobs Report problem πŸ“‹ Description β€’ Are you passionate about harnessing the power of generative AI and foundation models to build truly intelligent products? At Invoca, we're a team of innovators committed to building exceptional teams and groundbreaking AI solutions. β€’ This is a unique opportunity to architect the next generation of AI-powered customer experiences, making a direct and measurable impact on our products and the success of our clients. β€’ As a key member of our Data Platform team, you won't just build models; you'll architect the future of how businesses understand and interact with their customers. β€’ Design, build, and deploy scalable, production-grade applications using foundation models and other advanced AI techniques. β€’ Engineer robust Retrieval-Augmented Generation (RAG) pipelines and workflows that can reason, use tools, and solve complex problems. β€’ Establish and manage rigorous evaluation frameworks for complex AI agents. β€’ Craft, test, and manage sophisticated prompt chains to ensure optimal performance from our AI models. β€’ Develop resilient serving architectures that seamlessly integrate Large Language Models (LLMs) with enterprise systems, ensuring high availability and performance. β€’ Champion MLOps and CI/CD pipelines tailored for the unique challenges of generative AI. β€’ Translate customer needs into innovative AI-powered solutions as a strategic partner to product and engineering teams. 🎯 Requirements Proven Experience: 5+ years of professional experience in Applied AI Engineering, ML Engineering, or a closely related role with a strong focus on building and deploying AI-powered applications. β€’ Applied AI & Python Expertise: Advanced proficiency in Python and hands-on experience building applications with leading AI/ML frameworks (e.g., LangChain, LlamaIndex, CrewAI, PyTorch). You are an expert with data and ML libraries (e.g., Pandas, spaCy). β€’ Production Generative AI Champion: Demonstrated success deploying and maintaining applications powered by LLMs and other generative models in a production environment. β€’ Retrieval-Augmented Generation (RAG) Expertise: Deep, hands-on experience designing, building, and optimizing RAG pipelines. This includes expertise in vector databases (e.g., Qdrant, Pinecone, Weaviate), embedding strategies, and chunking techniques. β€’ Agentic AI System Evaluation Expertise: Demonstrable experience with modern evaluation techniques for multi-step AI agents. You should be able to speak to the trade-offs of evaluating reasoning traces, tool usage, and final outcomes using frameworks like LangSmith, DeepEval, Ragas, TruLens, or custom-built solutions. β€’ Prompt Engineering & In-Context Learning: Demonstrable skill in designing, testing, and optimizing complex prompts and few-shot examples to maximize model performance for specific tasks. β€’ Fine-Tuning Proficiency: Experience in fine-tuning foundation models for specific downstream tasks, with a clear understanding of when to fine-tune versus when to use in-context learning or agentic approaches. β€’ Scalable Model Serving: Advanced proficiency with API-driven frameworks for accessing and serving self-hosted foundation models (e.g., AWS SageMaker/Bedrock, Databricks Model Serving, TGI, vLLM), focusing on building resilient, scalable, and optimized integrations. β€’ Performance and Cost Optimization: A proven ability to optimize AI systems for low latency and high throughput. You have experience with techniques like model quantization, caching strategies, and infrastructure choices to manage and reduce operational costs. β€’ MLOps for AI Systems: Intermediate proficiency with MLOps tooling (e.g., MLFlow, Arize), and best practices for CI/CD, monitoring, and maintenance of complex AI systems. β€’ Educational Foundation: Bachelor's Degree in Computer Science, Engineering, Statistics, or a related field (or equivalent practical experience). An advanced degree (Master's or Ph.D.) is a strong plus. πŸ–οΈ Benefits Flexible Time Off β€’ Paid Holidays β€’ Health Benefits β€’ Retirement β€’ Stock Options β€’ Mental Health Program β€’ Paid Family Leave β€’ Paid Medical Leave β€’ InVacation β€’ Wellness Subsidy Apply Now