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About This Role
About Egen:
Egen is a fast\-growing and entrepreneurial company with a data\-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
*Want to learn more about life at Egen? Check out these resources in addition to the job description.*
Meet Egen
Life at Egen
Culture and Values at Egen
Career Development at Egen
Benefits at Egen
About the opportunity:
As a Senior AI Engineer, you will be at the forefront of our Generative AI initiatives. We treat AI as a software engineering discipline. You will be responsible for the full lifecycle of our AI features—specifically document intelligence and RAG pipelines—taking them from initial prototype to robust, scalable production services. You will solve for real\-world constraints like latency, error handling, and cost optimization.
You’ll collaborate with a diverse range of clients to translate business needs into high\-performance AI architectures. This role requires a blend of deep technical expertise in LLMs and a disciplined Software Engineering approach to ensure our solutions are robust, ethical, and scalable.
### What You Will Do:
- Optimize Inference: Build and tune production LLM serving with vLLM and SGLang—maximizing throughput and minimizing latency through batching, paged attention, quantization, and KV\-cache strategies
- Profile \& Accelerate Training: Instrument and profile training runs to find bottlenecks, then resolve them with the right attention implementations (e.g. FlashAttention) tuned to the underlying hardware (H200, GB200\)
- Engineer for the Hardware: Apply a working understanding of GPU architecture and attention internals to choose the right approach per accelerator, rather than relying on defaults
- Serve at Scale: Deploy and operate multiple models within shared GPU clusters on GKE, with autoscaling, efficient bin\-packing, and graceful handling of mixed workloads
- Drive Efficiency: Own GPU utilization as a first\-class metric—measure it, improve throughput\-per\-dollar, and continuously raise the ceiling on what our fleet can deliver
- Collaborate \& Consult: Work directly with clients to understand performance, latency, and cost requirements, and translate them into pragmatic serving and training architectures
### Your Technical Toolkit:
- Core Languages: Mastery of Python and shell scripting; comfort reading and reasoning about lower\-level (CUDA\-adjacent) performance code is a strong plus
- Inference Frameworks: Hands\-on experience with vLLM, SGLash, or comparable high\-performance serving stacks
- GPU \& Model Internals: Solid grasp of GPU architecture, the fundamentals of LLM inference, and the attention mechanism—including where the bottlenecks live and how FlashAttention and similar techniques address them across hardware generations (H200, GB200\)
- Profiling: Fluency with profiling tools to diagnose training and inference bottlenecks (compute\-bound vs. memory\-bound, kernel\-level analysis)
- Infrastructure: Strong Kubernetes (GKE) experience—deploying and autoscaling multiple models on shared GPU clusters on Google Cloud
- Mindset: A strong software engineering foundation—you write clean, maintainable code, measure before optimizing, and understand the full SDLC
### Basic Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
- 5\+ years of experience in ML/AI engineering, with a meaningful portion focused on performance, infrastructure, or systems
- Proven track record of deploying and optimizing models in a production environment
- Demonstrated experience profiling and improving GPU utilization for training and/or inference
- Experience with Classic Machine Learning (neural nets, training, tuning) is a strong plus
- Knowledge of Data Engineering and SQL
### Personal Attributes:
- Ownership: You take pride in your work and see optimizations through from profile to production
- Curiosity: Hardware and serving frameworks change fast; you are a lifelong learner who stays ahead of the curve
- Rigor: You measure before you optimize and let data, not intuition, guide where you spend effort
- Consultative Spirit: You enjoy interacting with clients and can translate technical complexity into business value
- Ethics: You prioritize responsible AI development and data privacy
$159,300 \- $250,100 a year
This position may be hired at multiple levels. Final leveling is determined during the interview process based on a candidate's experience, skills, and interview outcomes, and the applicable salary range will align with the final level assigned.
Compensation is determined based on factors including experience, expertise, interview performance, and geographic location, where applicable.
Compensation \& Benefits:
This role is eligible for our competitive salary and comprehensive benefits package to support your well\-being:
- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401 (k) Employer Match
- Employee Referral Bonuses
Check out our complete list of benefits here \- \>https://egen.ai/people/\#benefits
Important:All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations:
Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $159K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 Egen Solutions Inc, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($204K) sits 6% below the category median. Disclosed range: $159K to $250K.
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.
Egen Solutions Inc AI Hiring
Egen Solutions Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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.
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