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About This Role
Overview:
This position is hybrid in Peachtree Corners, Georgia and sits within our Product Development division, which develops, tests, and improves our software solutions in an innovative and collaborative environment.
The Opportunity
ConstructConnect is accelerating how AI is applied across our products, platforms, and engineering workflows. We are looking for an AI Engineer to design, build, and operate shared AI capabilities that other engineering teams can use to deliver secure, scalable, production\-ready solutions.
In this role, you will partner with software engineering, platform engineering, data, security, and product teams to make AI easier to adopt across the organization through practical services, tooling, and repeatable implementation patterns. This is a hands\-on engineering role for someone who enjoys building production systems, improving developer workflows, and helping turn promising AI concepts into reliable solutions for internal teams and customers.
Responsibilities:
What You’ll Be DoingPlatform \& Service Development
- Design, implement, and maintain shared AI platform components such as services, SDKs, templates, workflows, and reusable libraries that software engineering teams can adopt quickly and safely.
- Contribute to engineering patterns and paved paths for AI usage, including model access, prompt handling, evaluation, observability, security, and production support.
- Partner with product, application, data, and platform teams to translate AI use cases into scalable technical solutions instead of one\-off implementations.
- Build and operate reliable, cost\-aware AI and ML services on cloud platforms using containerized workloads, managed services, and modern infrastructure practices.
- Build and improve internal APIs, developer tooling, and integration patterns that simplify access to AI providers, model endpoints, retrieval services, and supporting data systems.
Operations \& Delivery
- Support end\-to\-end workflows for AI and ML use cases, including data preparation, experimentation, deployment, monitoring, and lifecycle management.
- Contribute to CI/CD practices for AI\-enabled services and ML components, including automated quality checks, security controls, and release guardrails.
- Instrument AI workloads with strong observability practices, including metrics, logs, dashboards, tracing, alerting, and cost visibility.
- Troubleshoot and resolve issues related to AI and ML deployments, including latency, scalability, integration failures, reliability problems, and cloud cost concerns.
Governance \& Enablement
- Partner with security, platform engineering, and architecture teams to ensure AI usage aligns with company policies for data classification, access control, privacy, and compliance.
- Evaluate emerging AI technologies, frameworks, and vendor capabilities, and share recommendations on where they may fit within ConstructConnect’s engineering roadmap.
- Contribute documentation, runbooks, onboarding materials, and reference implementations that help teams adopt AI capabilities with confidence.
- This job description in no way implies that the duties listed here are the only ones that team members can be required to perform.
Qualifications:
What You'll Be DoingRequired
- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical experience.
- 5–7 years of experience in software engineering, machine learning engineering, platform engineering, or a related area building and operating production systems.
- Strong proficiency in at least one modern programming language such as Python, Go, or TypeScript, along with solid software design, debugging, and engineering fundamentals.
- Experience building and operating services on a major cloud platform, preferably Google Cloud Platform, including familiarity with compute, storage, networking, and managed services.
- Hands\-on experience with containers and orchestration technologies such as Docker and Kubernetes.
- Experience with CI/CD pipelines and Git\-based engineering workflows used to build, test, and deploy services and platform components.
- Familiarity with infrastructure\-as\-code tools such as Terraform for provisioning and managing cloud resources in a repeatable, auditable way.
- Familiarity with MLOps concepts and tools used to support model training, evaluation, deployment, and monitoring.
- Understanding of modern AI capabilities such as generative AI, embeddings, retrieval patterns, NLP, and related ML concepts, and the ability to apply them responsibly in production environments.
- Experience building APIs, services, platforms, or libraries that are consumed by other engineers, with a focus on reliability, usability, and documentation.
- Strong foundation in observability and operational excellence, including experience managing service health through metrics, logs, dashboards, and alerting.
- Experience working cross\-functionally with product, data, infrastructure, platform, and security teams.
- Ability to translate technical topics into practical guidance and collaborate effectively in a distributed, remote\-friendly environment.
Preferred
- Experience supporting shared AI enablement, developer productivity, or platform engineering initiatives in a multi\-team SaaS environment.
- Experience with Google Cloud AI and data services such as Vertex AI, BigQuery, or related managed tooling.
- Familiarity with AI evaluation frameworks, guardrails, prompt management, model routing, and lifecycle governance.
- Experience working with vector search, retrieval\-augmented generation, agentic workflows, or orchestration frameworks in production settings.
- Exposure to intelligent search, recommendation systems, NLP, or computer vision use cases.
- Background working in environments where security, governance, and operational reliability are important to AI adoption.
Physical Demands and Work Environment:
- The physical activities of this position include frequent sitting, telephone communication, and working on a computer for extended periods. Visual acuity is required to perform activities close to the eyes.
- Team members are expected to maintain a dedicated and ergonomically appropriate remote workspace.
- Team members who live within commuting distance of one of our office locations (Greater Cincinnati/Northern Kentucky or Atlanta, Georgia) are expected to work in a hybrid capacity, with regular in\-office presence as determined by the team or department.
- All team members must reside and perform their work within the United States.
E\-Verify Statement
ConstructConnect utilizes the E\-Verify program with every potential new hire. This makes it possible for us to make certain that every employee who works for ConstructConnect is eligible to work in the United States. To learn more about E\-Verify you can call 1\-800\-255\-7688 or visit their website. E\-Verify® is a registered trademark of the United States Department of Homeland Security.
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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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ConstructConnect, 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ConstructConnect AI Hiring
ConstructConnect has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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