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About This Role
Title: AI Practice Lead
Location: Reston, VA (Remote)
Terms: Full\-time
Clearance: Must be a U.S. Citizen and able to obtain and maintain a Federal Security Clearance
Travel: Occasional (\<20%)
RESULTS. INNOVATION. VALUES. ACCOUNTABILITY.
That’s RIVA. Our employee\-first approach has manifested a culture that attracts the best and brightest. By investing in people first and providing a flexible work environment, our employees have higher morale, higher productivity rates, and lower turnover. At RIVA, people are our \#1 priority.
Program Overview
As a digital transformation leader in the federal IT space, RIVA Solutions delivers mission\-driven solutions across Application Development, Human\-Centered Design (HCD), Cybersecurity, Cloud \& Infrastructure, and Emerging Technology. Our corporate teams are the innovation engines that fuel that success — designing, building, and implementing enterprise\-grade solutions that empower agencies to achieve smarter, faster, and more secure outcomes.
This position is part of RIVA’s corporate Office of the Chief Technology Officer (CTO), a high\-impact group responsible for shaping the company’s technology vision, leading modernization initiatives, and advancing RIVA’s AI and data capabilities to serve federal customers and internal operations alike.
Position Overview
RIVA Solutions is seeking an innovative and hands\-on AI Practice Lead to architect, advance, and scale our next\-generation AI capabilities across our core business lines. This strategic and highly visible role combines thought leadership with the technical depth to prototype, design, and deploy cutting\-edge AI solutions that deliver measurable impact to both internal operations and federal customers.
The AI Practice Lead will work closely with the CTO, technical delivery teams, and business development organization to drive RIVA’s AI roadmap — building reusable frameworks, establishing governance, and ensuring responsible and secure AI integration across programs. The ideal candidate brings deep expertise in AI/ML system design, modern cloud architectures, and leadership experience in shaping enterprise\-wide AI strategy.
Core Responsibilities
- Define and lead the roadmap for RIVA’s AI Practice, ensuring alignment with company strategy and customer mission outcomes.
- Architect and deliver end\-to\-end AI solutions, including data ingestion, model design, training/tuning, deployment, and lifecycle governance.
- Build and scale AI/ML operations (MLOps) pipelines with automation, monitoring, and version control best practices.
- Serve as a hands\-on technical expert, designing proofs of concept (POCs) and implementing AI system components as needed.
- Partner with cross\-functional teams to design scalable, secure, and cloud\-native AI solutions using AWS and Microsoft Azure AI/ML services.
- Collaborate with BD/Sales teams to develop AI\-enabled proposals, build demonstrations, and engage with customers during pre\-sales activities.
- Establish and promote AI delivery standards, frameworks, and governance models emphasizing ethical and responsible AI use.
- Integrate cybersecurity, compliance, and data privacy standards into AI architectures.
- Track and evaluate emerging AI trends to identify new capabilities for adoption within RIVA’s delivery teams.
- Mentor and grow a multi\-disciplinary AI team of engineers, data scientists, and designers.
- Define and monitor KPIs to measure AI adoption, customer success, and delivery quality across projects.
Minimum Qualifications
- Bachelor’s or Master’s degree in Computer Science, Software Development, Engineering, or a related field.
- 10\+ years of experience delivering AI/ML\-based solutions, including at least 5 years in an architecture or technical leadership capacity.
- Proven hands\-on experience building AI prototypes, ML models, and production\-grade pipelines.
- Strong experience with AWS and Microsoft Azure AI/ML services and cloud\-native data architectures.
- Expertise in machine learning (ML), deep learning (DL), and large language model (LLM) deployment and governance.
- Familiarity with MLOps practices, continuous integration, and model lifecycle management.
- Experience applying human\-centered design principles to AI\-driven solutions.
- Knowledge of cybersecurity and data compliance considerations in AI environments.
- Strong collaboration skills and ability to interface with both technical and executive stakeholders.
- Excellent written and verbal communication skills.
- U.S. Citizenship and the ability to obtain a Federal Security Clearance.
Preferred Qualifications
- Advanced degree in Artificial Intelligence, Machine Learning, or Software Development.
- Experience deploying Generative AI (GenAI) or LLM\-based solutions in enterprise or federal environments.
- Background building or scaling AI practices, Centers of Excellence (CoEs), or reusable AI frameworks.
- Proficiency with Infrastructure as Code (IaC), containerization, and scalable microservice architectures.
- Familiarity with low\-code/no\-code AI platforms or workflow automation tools.
Salary
$200K (Depending on experience)
RIVA Benefits
- Paid Time Off / Sick Leave
- Health, Dental, and Vision Coverage
- Life Insurance
- 401K Retirement Plan with Company Match
- HSA/FSA Spending Accounts
- Long\- and Short\-term Disability
- Pet Insurance
- Wellness Program Initiatives
- RIVA Flex (Flexible Hours and Hybrid Work Support)
- Additional Perks \& Workplace Benefits
Equal Opportunity Statement
RIVA Solutions is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any protected class. If you need a reasonable accommodation to search for a job opening or to submit an online application, please email [email protected]. Only messages left for this purpose will be returned.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Riva 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Riva Solutions Inc AI Hiring
Riva Solutions Inc 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 $214,343 across 143 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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