Job description

About SmartLogic SmartLogic is a U.S.-based custom software development consultancy serving clients since 2005. We build and maintain custom web and mobile applications and are looking to intentionally modernize how our engineering team builds software through effective, responsible use of AI-assisted development tools and practices. What We Are Looking For We are seeking an experienced AI engineering consultant or practitioner to help SmartLogic move from individual, ad hoc AI usage to a more intentional, repeatable, team-wide approach to AI-assisted software development. This is not a management role or an outsourced engineering leadership position. We are looking for someone who can work directly with our Staff Engineers and developers to assess how we work today, identify meaningful opportunities for improvement, and help our team build the skills, standards, and workflows needed to use AI effectively in production software development. This is intended to help upskill our existing staff with modern AI practices. Business Objective Our goal is simple: Build high-quality software faster and better than we can using traditional development methods alone. We believe effective AI adoption can help us: • Increase engineering capacity without increasing headcount at the same rate. • Improve delivery efficiency and project margins. • Reduce writedowns, rework, and avoidable delivery costs. • Keep our engineering team current and competitive as software development practices evolve. • Create consistent, responsible AI practices across project teams. We do not expect a consultant to arrive with a predetermined AI ROI formula. Part of this engagement is helping us establish realistic baselines and determine what measures are actually useful for evaluating improvements in delivery speed, quality, and engineering capacity. Scope of Work We expect the engagement to include: • Assess: • Current AI usage, engineering workflows, skill levels, pain points, and opportunities across the team. • Identify the highest-value use cases for AI-assisted development within our environment. • Evaluate AI tools and practices and recommend where SmartLogic should standardize, experiment, or avoid adoption. • Plan: • Develop a practical AI engineering enablement upskill strategy and prioritized roadmap. • Establish repeatable practices, standards, playbooks, workflows, or reusable tooling that can be adopted across project teams. • Coach: • Upskill developers and Staff Engineers through hands-on application of AI in real development workflows that shows measured efficiencies. • Ensure AI-assisted development maintains strong engineering standards around architecture, maintainability, testing, code review, security, privacy, and quality. • Measure: • Help establish appropriate baselines and measures for assessing whether AI adoption is meaningfully improving software delivery profit margin. Expected Outcomes At the end of the initial engagement, we want to have: • A clear understanding of our current AI maturity and highest-value opportunities. • A prioritized AI adoption roadmap. • Defined standards and practices for responsible AI-assisted software development. • Developers and Staff Engineers who are more capable and confident using AI effectively in production work. • Repeatable workflows and practices that extend beyond individual power users. • A practical approach for evaluating whether these changes are helping us build quality software faster and more efficiently. Our Environment SmartLogic has 8 engineers, including 2-3 staff engineers, 1 project manager, 1 QA specialist, and 1 designer working across multiple client projects at any given time. Our primary stack is Elixir/Phoenix, with additional work in Ruby on Rails, ReactNative and sometimes Flutter. Current AI usage is individual and uneven. Client Work Considerations Most of our engineering work happens in client codebases. Client contracts vary in how they permit AI tool usage, and some involve sensitive data, including health information. Any recommended practices must account for client IP, confidentiality obligations, and differing permissions across projects. The selected partner will be required to sign an NDA. Ideal Partner We are looking for someone with: • Staff Engineer-level software engineering depth and judgment. You should be strong enough in traditional software engineering practices to effectively coach experienced Staff Engineers and senior developers, not simply teach AI tools. • Deep, hands-on experience using AI-assisted development and agentic coding tools in real production codebases. • Demonstrated ability to help engineers use AI without lowering the bar for architecture, maintainability, testing, security, code review, and overall software quality. • Experience helping engineering teams adopt AI, not simply using it personally. • Experience developing practical workflows, standards, playbooks, or internal tooling that other engineers actually use. • Strong understanding of the risks and limitations of AI-generated software, including when AI assistance should not be used. • Experience evaluating the impact of AI on software delivery without relying solely on superficial adoption metrics. • Strong coaching and communication skills, including the ability to influence and develop experienced engineers with established technical practices. Proposal Response Please include: • Relevant experience helping software engineering teams adopt AI-assisted development. • Examples of similar engagements and outcomes. • Your proposed approach for SmartLogic. • Who would perform the work and their relevant technical background. • Recommended engagement structure and timeline. • Pricing and payment structure. • References or relevant case studies, if available. Submission Details Please submit proposals as a PDF to Bri Bellavati, bri@smartlogic.io by October 23rd.

Requirements

Must-haves

Current AI usage, engineering workflows, skill levels, pain points, and opportunities across the team.
Identify the highest-value use cases for AI-assisted development within our environment.
Evaluate AI tools and practices and recommend where SmartLogic should standardize, experiment, or avoid adoption.
Develop a practical AI engineering enablement upskill strategy and prioritized roadmap.
Establish repeatable practices, standards, playbooks, workflows, or reusable tooling that can be adopted across project teams.
Upskill developers and Staff Engineers through hands-on application of AI in real development workflows that shows measured efficiencies.
Ensure AI-assisted development maintains strong engineering standards around architecture, maintainability, testing, code review, security, privacy, and quality.
Help establish appropriate baselines and measures for assessing whether AI adoption is meaningfully improving software delivery profit margin.
Expected Outcomes
At the end of the initial engagement, we want to have:
A clear understanding of our current AI maturity and highest-value opportunities.
A prioritized AI adoption roadmap.
Defined standards and practices for responsible AI-assisted software development.
Developers and Staff Engineers who are more capable and confident using AI effectively in production work.
Repeatable workflows and practices that extend beyond individual power users.
A practical approach for evaluating whether these changes are helping us build quality software faster and more efficiently.
Our Environment
Client Work Considerations
Ideal Partner
Deep, hands-on experience using AI-assisted development and agentic coding tools in real production codebases.
Demonstrated ability to help engineers use AI without lowering the bar for architecture, maintainability, testing, security, code review, and overall software quality.
Experience helping engineering teams adopt AI, not simply using it personally.
Experience developing practical workflows, standards, playbooks, or internal tooling that other engineers actually use.
Strong understanding of the risks and limitations of AI-generated software, including when AI assistance should not be used.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: workable
Effort: medium