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About This Role
AI Engineer – .NET & Generative AI | Houston Hybrid
Location: Houston, TX Employment Type: Full-Time, Direct Hire Work Arrangement: Hybrid – Minimum 2 Days Onsite / Client Sites as Needed
Please Read Before Applying
This is a Houston-based hybrid position and is not a fully remote opportunity.
Candidates must currently reside in the Greater Houston area and be available to work onsite a minimum of 2 days per week, including at client locations as needed.
We are not considering relocation candidates for this opening.
This role is designed for a hands-on software engineer who combines strong application-development fundamentals with experience building or integrating modern AI solutions. This is not a data science, research, prompt-engineering-only, or purely ML-modeling position.
Candidates should have professional C#/.NET development experience and be comfortable working directly with business stakeholders to understand problems and translate them into working technical solutions.
Employment sponsorship is not available now or in the future.
About the Role
You'll be embedded on client teams as the technical builder responsible for turning scoped use cases into working systems.
That means hands-on, full-stack implementation—including RAG pipelines, context management, agentic workflows, APIs, enterprise integrations, and applications that connect with clients' existing infrastructure.
You'll work closely with client stakeholders to understand their business processes and translate those needs into technical solutions, but your primary output is the build itself.
What You'll Do
- Build AI Solutions: Architect and develop production-ready AI solutions, including RAG systems, agentic workflows, and full-stack applications.
- Integrate With Existing Systems: Connect AI capabilities into established client infrastructure without requiring wholesale system replacement or data migration.
- Translate Business Problems: Work directly with business stakeholders to understand operational challenges and translate them into technical requirements and system designs.
- Develop Hands-On: Serve as the core technical builder on cross-functional client delivery teams.
- Design Context & Retrieval Systems: Build and manage context pipelines, retrieval systems, vector search, and data flows that keep AI outputs accurate and grounded.
- Deliver Production Systems: Build, test, integrate, and ship solutions against client engagement timelines.
- Communicate Technical Decisions: Explain architecture, technical tradeoffs, and recommendations clearly to both technical and nontechnical stakeholders.
- Enable Client Teams: Document systems and provide knowledge transfer so client teams can maintain and extend what has been built.
Required Qualifications
Candidates should meet the following requirements:
- Professional, hands-on C#/.NET software development experience
- Strong software engineering fundamentals and experience building applications, APIs, integrations, or enterprise systems
- Experience working directly with clients or business stakeholders to understand business problems and translate them into technical solutions
- Ability to operate in ambiguous environments where requirements may not already be fully defined
- Strong written and verbal communication skills
- Ability and willingness to work onsite in Houston at least 2 days per week, including at client locations as needed
- Currently located in the Greater Houston area
- Authorized to work in the United States without current or future employer sponsorship
Strongly Preferred
- Modern .NET 8 or .NET 10
- Professional experience building or implementing Generative AI / LLM applications
- RAG systems
- Vector databases and embeddings
- Retrieval pipelines and context management
- Agentic AI frameworks
- Tool/function calling and orchestration
- Blazor
- Full-stack application development
- Enterprise system integration
- Technical consulting or client-delivery experience
What We're Looking For
We're especially interested in engineers who can operate at the intersection of software engineering, AI, and business problem-solving.
You should be comfortable moving between conversations with business stakeholders and hands-on technical implementation. You don't need to have worked with every AI framework or tool listed above, but you should have a strong software engineering foundation, the ability to learn quickly, and experience delivering real technical solutions.
This position may be a strong fit if you've spent your career building enterprise software in C#/.NET and have recently begun implementing Generative AI capabilities, or if you're already building production AI applications within enterprise environments.
Pay: $100,000.00 - $150,000.00 per year
Benefits:
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Application Question(s):
- Do you currently reside in the Greater Houston, Texas area?
- Are you able to work onsite in Houston at least two days per week, including at client locations as needed?
- How many years of professional experience do you have developing applications using C#/.NET?
- Do you have professional experience working directly with clients or business stakeholders to translate business requirements into technical solutions?
Work Location: Hybrid remote in Houston, TX 77007
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Continuum Solutions, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Continuum Solutions AI Hiring
Continuum Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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