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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, [email protected] by October 23rd.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Smartlogic, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Smartlogic AI Hiring
Smartlogic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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