Position Title: AI Engineering Lead
Position Location: Columbia Illinois-Inline Data Systems- (Hybrid, 4 DAYS in the office 1 DAY from home after probationary period)
Position Status: Salay Exempt
Reporting to: Director of Software Development
Compensation: $110,000-$150,000 per year based on experience
Company Summary:
Inline Data Systems an affiliate of Automotive Product Consulants gives sellers and administrators of extended warranties a dynamic CRM with F&I tools to dominate the industry. We’ve worked the same roles you have, so we built our systems with all the functionality you need—and features you didn’t know you wanted.
Role Summary
This position will lead the integration of artificial intelligence capabilities into Inline Data Systems'' core software products — the Inline Administration platform and Inline CRM — including AI-driven call center features (automated calling and real-time quoting) and AI-driven automated sales experiences on customer-facing websites. This role also drives the adoption of AI-assisted tools and practices across the software development lifecycle. This role bridges applied AI/ML engineering with developer enablement, with the goal of delivering smarter product features and a measurably faster, more productive engineering team.
Key Responsibilities:
1. AI Feature Design and Implementation: Product AI Integration:
Identify, prioritize, and design opportunities to embed AI capabilities into the Inline Administration and Inline CRM platforms, such as intelligent automation, predictive analytics, natural language interfaces, and recommendation or lead-scoring engines.
Build and ship AI-powered features, including conversational assistants, automated data classification/entry, anomaly detection, and smart search.
Integrate with large language model (LLM) APIs (e.g., Anthropic Claude, OpenAI) and manage prompt design, retrieval-augmented generation (RAG) pipelines, and, where appropriate, model fine-tuning.
Ensure AI-driven features are accurate, reliable, secure, and compliant with data privacy requirements.
Call Center and Sales Automation Features:
Design and implement AI-driven calling capabilities for CRM call center customers, including automated outbound/inbound dialing and conversational voice AI to support agent-led and self-service sales calls.
Build AI-assisted quoting tools that generate accurate, real-time product quotes during live customer calls, pulling from CRM and pricing data.
Provide real-time agent assistance during calls, including live transcription, sentiment analysis, and next-best-action or upsell recommendations.
Design and implement AI-driven automated sales experiences on customer-facing websites, including conversational sales assistants, dynamic quoting, and guided checkout flows.
Integrate AI calling and website sales features with telephony/contact center platforms (e.g., Twilio, Amazon Connect) and CRM data to personalize offers, pricing, and follow-up.
Monitor and continuously improve AI-driven call and website sales conversion performance based on outcome data.
2. Developer Productivity and AI Tooling:
AI-Assisted Development Enablement:
Champion and drive adoption of AI coding assistants (e.g., Claude Code, GitHub Copilot) across the development team to accelerate coding, code review, testing, and documentation.
Establish best practices, guidelines, and internal training for effective use of AI tools throughout the software development lifecycle.
Build internal tooling and workflows that apply AI to code generation, automated testing, bug triage, and technical documentation.
Track and report on productivity gains from AI tool adoption, such as cycle time, code review turnaround, and defect rates.
3. AI Strategy and Roadmap:
Partner with product and engineering leadership to define and maintain the AI roadmap for company software products.
Evaluate emerging AI/ML technologies, tools, and vendors for applicability to business needs.
Recommend build-vs-buy decisions for AI capabilities, including self-hosted models versus third-party APIs.
4. Data and Model Management:
Oversee data pipelines needed to support AI features, including data cleaning, labeling, embeddings, and vector database management.
Monitor the accuracy and performance of AI features in production and iterate based on user feedback and usage metrics.
Manage usage and costs of LLM/AI API consumption across products.
5. Security, Compliance, and Responsible AI:
Ensure AI features handle customer and administrative data responsibly and in compliance with relevant data privacy regulations.
Implement guardrails to mitigate AI misuse, hallucination-driven errors, and biased or inappropriate outputs in customer-facing features.
6. Collaboration and Communication:
Work closely with developers, product managers, and QA to embed AI features and productivity tooling into existing workflows.
Provide hands-on training and mentorship to developers on AI-assisted coding practices.
Communicate AI initiatives, timelines, risks, and outcomes to executive leadership.
Knowledge/Experience/Education/Training:
Bachelor''''s degree in Computer Science, AI/ML, or a related field, or equivalent practical experience.
Proven experience integrating AI or LLM capabilities into production software products.
Hands-on experience with LLM APIs (e.g., Anthropic Claude, OpenAI), prompt engineering, and retrieval-augmented generation (RAG) architectures.
Experience driving adoption of AI coding assistants and developer productivity tooling (e.g., Claude Code, GitHub Copilot).
Proficiency in Python and/or .NET (C#) for AI/ML integration work.
Familiarity with vector databases and embeddings (e.g., Pinecone, pgvector).
Experience with cloud AI/ML services such as AWS Bedrock or SageMaker is a plus.
Familiarity with CRM and business administration software workflows is a plus.
Experience with conversational or voice AI platforms and telephony/contact center integrations (e.g., Twilio, Amazon Connect, IVR systems) is a plus.
Experience building AI-driven sales or e-commerce experiences, such as chat-based sales assistants, dynamic quoting, or pricing engines, is a plus.
Abilities:
Must be able to translate business needs into practical, shippable AI solutions.
Must possess strong communication skills to explain AI capabilities and limitations to non-technical stakeholders.
Must be able to work independently and lead cross-functional AI initiatives.
Must have a strong desire to continuously learn and evaluate new AI technologies.
Ability to analyze and translate complex business processes into automatable, AI-augmented workflows.
Ability to measure and report on AI feature adoption and developer productivity impact using data-driven methods.
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