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About This Role
- Atlanta
- Full time
What We’re About
At Mimacom, we turn complex business challenges into digital solutions that create measurable value. As part of our growing North American business, you'll work alongside experienced engineers, architects, consultants, and business leaders to help organizations unlock the value of AI, data, and cloud technologies.
We are looking for an AI Growth Engineer (Solutions Architect) to join our US team. This is a unique opportunity for a hands-on technologistwho is equally comfortable engaging with customers, shaping solution strategies, building prototypes, and delivering enterprise-grade software solutions. You will work closely with our US business leadership to help grow Mimacom's AI, data, and cloud practice while remaining actively involved in customer delivery through go-live.
This role is best suited for a senior hands-on architect or lead engineer who has evolved beyond pure software development and enjoys combining customer engagement, solution design, AI innovation, and technical leadership. While expertise across every discipline is not expected, candidates should have a strong architectural mindset and be comfortable operating across business and technical domains.
What You’ll Do
As an AI Growth Engineer (Solutions Architect), you will act as the technical bridge between business opportunities and successful project delivery. Combining the customer-facing capabilities of an AI Architect with the engineering expertise of a Senior Engineer, you will help customers solve complex business challenges through modern AI-poweredand data-driven solutions.
- Join customer engagements from the earliest stages, uncovering business and technical requirements and helping define solution strategies.
- Shape proposals, solution architectures, delivery approaches, effort estimates, and team structures to support successful sales opportunities.
- Build MVPs, proofs of concept (PoCs), demos, and prototypes that demonstrate business value and accelerate decision-making.
- Design and architect enterprise-grade AI, data, and cloud solutions from concept through production deployment.
- Build AI-enabled systems leveraging Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and context engineering techniques.
- Design and implement modern data platforms, event-driven architectures, streaming architectures, search solutions, and analytics capabilities.
- Work hands-on throughout the delivery lifecycle, contributing code, reviewing implementations, and mentoring technical teams.
- Leverage AI-assisted engineering tools to accelerate development and help customers adopt modern engineering practices.
- Collaborate closely with customers and cross-functional teams to ensure successful project delivery and go-live.
- Act as a trusted technical advisor for stakeholders ranging from engineering teams to executive leadership.
What You’ll Bring
- Degree in Computer Science or equivalent practical experience.
- 5+ years of hands-on software engineering experience, ideally 8+ years within complex enterprise environments.
- Strong communication and stakeholder management skills, with the ability to engage both technical and business audiences.
- Experience supporting customer workshops, solution consulting, proposal creation, and effort estimationactivities.
- Proven ability to rapidly build prototypes and MVPs and evolve them into production-ready solutions.
- Practical experience integrating and deploying Generative AI solutions, including LLMs, prompt engineering, context engineering, and AI-assisted development tools.
- Experience designing and building distributed systems and microservices architectures on AWS and/or Azure.
- Hands-on experience with Apache Kafka in enterprise environments is required.
- Strong experience with Elasticsearch is highly preferred.
- Experience with Databricks is a plus.
- Experience with Snowflake is nice to have.
- Experience defining solution architectures and supporting enterprise architecture discussions, including technology roadmaps, integration strategies, and modernization initiatives.
- Strong expertise in Java and Spring Boot.
- Familiarity with React or Angular and their role within enterprise application architectures.
- Authorization to work in the United States.
- Fluency in English.
Nice to Have
- Deep enterprise architecture experience, including formal architecture governance frameworks, large-scale IT landscape assessments, and target-state transformation programs.
- Consulting or professional services experience working across multiple clients and projects.
- Certifications in AWS, Azure, Databricks, Confluent, Elastic, or related technologies.
- Experience within financial services, insurance, retail, manufacturing, or life sciences.
- Knowledge of DataOps, CI/CD practices, and automated data pipelines.
- Experience with Liferay or other enterprise platforms.
- Public speaking, technical writing, conference presentations, or open-source contributions.
- German or Spanish language skills for collaboration with European Mimacom teams.
What Awaits You
- A highly entrepreneurial role combining business growth, technical consulting, solution architecture, and hands-on engineering.
- The opportunity to work with cutting-edge AI, data, and cloud technologies across a variety of industries and customer environments.
- Direct involvement in shaping and growing Mimacom's AI and data business in North America.
- Collaboration with talented colleagues across the United States and Europe.
- A role split of approximately 40% pre-sales and business growthactivities and 60% solution design and delivery.
- Regular travel to customer sites as required.
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 mimacom, 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 $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.
mimacom AI Hiring
mimacom 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, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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