Senior Applied AI Engineer

Job Description

***100% remote EXCEPT in Washington, Louisiana, Wyoming, West Virginia, South Dakota, Hawaii, Alaska, Puerto Rico, Washington DC, Ohio, New Mexico, Maine, Rhode Island, Vermont, Illinois, Colorado, Massachusetts, Montana***

This role will play a critical part in designing, building, and deploying enterprise-scale Generative AI solutions that enhance customer experiences, improve operational efficiency, and enable intelligent automation across the business. This is an opportunity to work on real-world AI applications from concept through production, leveraging the latest advancements in large language models, conversational AI, retrieval systems, and intelligent automation. The ideal candidate is a hands-on builder with recent experience deploying production-grade GenAI solutions and thrives in a fast-paced, collaborative environment.

Responsibilities

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• Design, prototype, and deploy Generative AI solutions across customer-facing and internal business applications.
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• Develop and optimize applications powered by large language models (LLMs), vector databases, prompt engineering techniques, and Retrieval-Augmented Generation (RAG) architectures.
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• Build, deploy, and maintain AI-powered chatbots and virtual assistants, ensuring scalability, reliability, security, and compliance.
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• Lead the development of AI agents for both digital and voice-based experiences, supporting real-time interactions for customers and employees.
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• Evaluate, integrate, and implement third-party AI platforms, APIs, and emerging technologies to accelerate innovation and business value.
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• Create proof-of-concept applications to assess new use cases such as document processing, classification, summarization, knowledge management, and workflow automation.
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• Optimize prompts, model configurations, and orchestration frameworks to improve performance, accuracy, and cost efficiency.
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• Partner closely with data, platform, and MLOps teams to ensure robust deployment processes, monitoring, observability, and governance practices.
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• Collaborate with product managers, engineers, and business stakeholders to translate user needs into scalable AI solutions.
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• Contribute to architectural discussions and establish best practices for enterprise AI adoption and integration.
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Qualifications

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• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related technical field.
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• Demonstrated success building and deploying production-grade AI chatbots, assistants, or conversational AI solutions within the last two years.
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• 6+ years of experience in AI/ML engineering, software engineering, data engineering, or natural language processing, including recent hands-on work with Generative AI and LLM-based applications.
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Required Skills

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• Advanced Python programming skills and experience with modern AI frameworks and libraries such as LangChain, Hugging Face, OpenAI, Transformers, or similar technologies.
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• Experience working with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or equivalent platforms.
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• Strong understanding of RAG architectures, prompt engineering, model evaluation, API orchestration, and LLM optimization techniques.
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• Solid SQL skills and experience working with cloud-based data platforms and large-scale datasets.
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• Ability to operate effectively in cross-functional teams and collaborate with product, engineering, analytics, and external partners.
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• Strong communication skills with the ability to explain complex AI concepts and outcomes to both technical and non-technical audiences.
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Preferred Qualifications

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• Experience working in highly regulated industries such as financial services, healthcare, insurance, or telecommunications.
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• Hands-on experience developing voice AI, speech-enabled assistants, or conversational systems integrated with telephony platforms.
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• Familiarity with modern data stack technologies, including cloud data warehouses, transformation tools, and data orchestration frameworks.
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• Experience integrating AI solutions with CRM, customer service, or workflow management platforms.
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• Exposure to document intelligence use cases including OCR, information extraction, summarization, semantic search, and personalized recommendations.
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• Experience supporting customer-facing applications, contact center technologies, or enterprise-scale service operations.
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