Builder - Senior Software Engineer, AI

CV in; Northern (KY); Hybrid; USD 140,000 - 200,000; Full time

Job summary

CV in is seeking an experienced AI systems engineer to architect and deliver end-to-end AI capabilities powering our revenue orchestration platform. You will own the lifecycle of intelligent features from concept to production deployment, partnering with product and data teams to translate complex AI requirements into scalable software solutions.

The role requires deep expertise in large language models, retrieval augmented generation, and production ML operations, with a focus on safety,

Qualifications

• : 5+ years software engineering focused on AI/ML in production.

Responsibilities

• Architect and implement retrieval augmented generation systems for revenue workflows.
• Construct evaluation frameworks and benchmarks for AI models in production.
• Design interfaces that unlock LLM integrations for go-to-market teams.
• Establish data collection pipelines, labeling strategies, and feedback loops.
• Partner with product, design, and data teams to identify AI value opportunities.
• Engineer reusable AI infrastructure to speed feature development and reduce time to market.
• Implement AI safety mechanisms, monitoring dashboards, and QA processes.
• Showcase AI features to internal audiences and gather feedback for roadmap.
• Keep current with AI research to keep products cutting edge.
• Collaborate with data scientists to translate experiments into production services.
• Optimize system performance, latency, and reliability for AI workloads.
• Document designs and APIs for long-term ownership.
• Lead code reviews and mentor junior engineers.

Skills

LLM systems

Retrieval augmented generation

Production ML

Model deployment

Prompt engineering

System performance

Vector databases

Semantic search

AI safety

Cross-functional collaboration

Tools

MLflow

Weights & Biases

Kubeflow

Job description

You will architect and deliver the core AI capabilities that power our revenue orchestration platform, owning the end to end lifecycle of intelligent features from initial concept through production deployment. You will partner with product and data teams to translate complex AI requirements into robust, scalable software solutions that directly enhance how our customers manage go to market operations. This role demands deep expertise in designing and implementing large language model based systems that are performant, reliable, and secure in a production environment. You will define and enforce standards for AI evaluation, safety, and monitoring to ensure our products meet the highest quality benchmarks. Your work will involve building the foundational infrastructure that allows other teams to rapidly prototype and ship AI powered experiences. You will lead technical design discussions and make critical decisions that shape the architecture of our AI platform. Through mentorship and collaboration, you will elevate the engineering bar across the team and contribute to the technical vision of the company. If you are driven by building intelligent systems that solve hard real world problems, this role is where you will have maximum impact from day one.

Key facts

Location: USA

Engagement: Full-time

What you'll do

• Architect and implement retrieval augmented generation systems that deliver accurate and context aware responses for our revenue workflows.
• Construct evaluation frameworks and benchmarks to rigorously measure the quality and performance of AI models in production scenarios.
• Design novel interaction paradigms and user interfaces that unlock the full potential of large language model integrations for go to market teams.
• Establish data collection pipelines, labeling strategies, and feedback loops that enable continuous improvement of our AI models over time.
• Partner with cross functional stakeholders to uncover opportunities where artificial intelligence can generate measurable value for our customers.
• Engineer reusable AI infrastructure and platform components that accelerate feature development and reduce time to market for new capabilities.
• Implement robust AI safety mechanisms, monitoring dashboards, and quality assurance processes to ensure reliable and responsible system behavior.
• Showcase AI features and prototypes to internal audiences, synthesizing feedback to guide product refinements and roadmap priorities.
• Track and integrate advances from the latest AI and machine learning research to keep our product offerings at the cutting edge.
• Collaborate with data scientists and researchers to translate experimental models into scalable and maintainable production services.
• Optimize system performance, latency, and reliability for AI workloads to meet stringent operational and user experience standards.
• Document technical designs and APIs to ensure clarity and maintainability for long term ownership by the team.
• Lead code reviews and technical discussions to uphold high standards of software quality, testing, and operational excellence.
• Mentor engineers and work closely with junior team members to strengthen the overall AI engineering capability of the group.

Requirements

• Bring 5 or more years of software engineering experience, with a substantial focus on building AI and machine learning systems in production.
• Demonstrate strong expertise in large language models, retrieval augmented generation techniques, and modern AI frameworks and libraries.
• Show proven experience with ML operations, including model deployment, versioning, and ongoing AI system monitoring in live environments.
• Have a history of building production grade AI applications, successfully moving experiments from research phases to stable deployments.
• Exhibit deep understanding of prompt engineering strategies, model fine tuning approaches, and rigorous AI evaluation methodologies.
• Proficiency in analyzing AI system performance metrics, diagnosing issues, and implementing targeted improvements.
• Hands on experience with vector databases, embedding systems, and semantic search implementations at scale.
• Solid knowledge of AI safety practices, responsible AI principles, and methods for mitigating risks in generative AI systems.
• Strong communication skills, with the ability to explain intricate AI concepts clearly to both technical and non technical stakeholders.
• Comfort working in a fast paced, collaborative environment where priorities can shift based on customer needs and market opportunities.
• Willingness to engage in deep technical problem solving and contribute to architectural decisions that impact the entire platform.
• Commitment to writing clean, maintainable code and following software engineering best practices in all deliverables.
• Ability to collaborate effectively with cross functional teams, including product managers, designers, and data scientists.
• Willingness to learn and adopt new tools, frameworks, and processes as the AI landscape continues to evolve rapidly.