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Compunnel, Inc.
Senior AI Engineer
Career Insights for Natural Language Processing Engineer
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Based on Kentucky data
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What they do
A Natural Language Processing Engineer specializes in developing and implementing algorithms and models tailored for understanding, processing, and generating natural language text. They utilize methodologies such as tokenization, parsing, named entity recognition, part-of-speech tagging, and other NLP techniques to perform tasks including text classification, chatbot development, and other applications where the primary input or output is natural language text.
$117,055 / year median in Kentucky
Job Description
Job Summary The Senior AI Engineer will be an early member of a new AI engineering organization focused on designing, building, and deploying innovative AI solutions that improve advisor and client experiences. The role will leverage Generative AI, large language models (LLMs), automation, advanced analytics, and modern cloud technologies to solve business problems, streamline workflows, reduce administrative overhead, and improve productivity. The ideal candidate is a hands-on engineer with strong AI, software engineering, data engineering, and cloud expertise who can develop secure, scalable, enterprise-grade AI solutions from concept through production. Key Responsibilities
- Design, build, and deliver AI-powered solutions from concept through production.
- Develop AI capabilities that improve advisor effectiveness, client engagement, productivity, and workflow efficiency.
- Build solutions for use cases including automated meeting preparation, intelligent call summarization, follow-up automation, knowledge retrieval, recommendation systems, decision support, and workflow orchestration.
- Develop AI-powered applications using LLMs, agent frameworks, and orchestration platforms such as OpenAI, Claude, Bedrock, LangChain, and LangGraph.
- Design and implement Retrieval-Augmented Generation (RAG), semantic search, and enterprise knowledge solutions using vector databases and retrieval frameworks.
- Develop scalable full-stack applications and services using Python, TypeScript, Node.js, APIs, React, and Next.js.
- Deploy and scale AI applications across AWS, Azure, or Google Cloud environments.
- Apply modern platform engineering and DevOps practices, including containers, Kubernetes, Infrastructure-as-Code, and cloud-native architectures.
- Collaborate with business partners, product teams, architects, data scientists, and engineering teams to identify high-value AI opportunities.
- Translate business requirements into secure, scalable, production-ready AI applications.
- Apply software architecture, design patterns, security, and reliability principles to enterprise-scale AI solutions.
- Troubleshoot complex technical challenges and drive AI solutions through successful production deployment.
- Contribute to the evolution of modern AI platforms, engineering practices, and capabilities. Required Qualifications
- Bachelor's degree or equivalent experience with 5+ years of software engineering experience.
- Proven experience designing and delivering scalable, production-grade software solutions and distributed systems.
- Deep hands-on experience building AI-powered applications utilizing LLMs, agent frameworks, and orchestration platforms.
- Strong experience with technologies such as OpenAI, Claude, Bedrock, LangChain, and/or LangGraph.
- Experience developing Retrieval-Augmented Generation (RAG) solutions, semantic search capabilities, and enterprise knowledge systems.
- Experience working with vector databases and retrieval frameworks.
- Strong full-stack engineering experience with Python, TypeScript, Node.js, APIs, React, and/or Next.js.
- Hands-on experience deploying and scaling applications in cloud environments such as AWS, Azure, or Google Cloud.
- Experience with modern platform engineering and DevOps practices, including containers, Kubernetes, Infrastructure-as-Code, and cloud-native architectures.
- Strong understanding of software architecture, design patterns, security, and reliability principles for enterprise-scale applications.
- Excellent problem-solving skills, sound technical judgment, and ability to address complex technical challenges.
- Ability to collaborate effectively across engineering, data, product, and business teams while driving initiatives from concept through production.