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About This Role
Job Title: Senior AI Engineer
Location: Remote, USA
Clearance Required: Active Secret Clearance
Position Type: Full-Time
About the Company
At VivSoft, we aim to solve complex federal problems using emerging and open technologies in a collaborative and rewarding environment. VivSoft is a diverse team of strategists, engineers, designers, and creators experienced in building high-performance, effective software, with a focus on impactful organizational design and software delivery dynamics. We build secure Software Factories based on DoD reference designs and NIST Frameworks for Cloud and DevSecOps. These factories deliver AI/ML Applications, Data Science Platforms, Blockchain, and Microservices for DoD, Healthcare, and Civilian Agencies.
Job Summary
We are seeking a Senior AI Engineer to design, develop, and deploy advanced Artificial Intelligence and Machine Learning (AI/ML) solutions within complex, large-scale data environments. This role will focus on building distributed machine learning models, developing agentic AI workflows, and integrating Large Language Models (LLMs) with enterprise data platforms to support predictive analytics, intelligent automation, and data-driven decision-making.
The ideal candidate will have extensive experience in software engineering, data engineering, and applied AI/ML, with expertise in distributed computing, statistical modeling, and modern AI technologies. This individual will work closely with data engineers, data scientists, and cloud/platform engineering teams to develop scalable AI solutions, optimize model performance, and deploy AI/ML capabilities within secure and disconnected environments.
Key Responsibilities
- Design, develop, and deploy distributed machine learning models using Apache Spark and Apache Iceberg, applying rigorous statistical validation and classical machine learning techniques where appropriate.
- Build and implement agentic AI workflows using LLMs, tool calling, Model Context Protocol (MCP), retrieval-augmented generation (RAG), and multi-step planning to query and analyze enterprise datasets.
- Develop and optimize AI-driven data analysis workflows that leverage distributed query engines such as Amazon Athena, Trino, and Spark SQL.
- Optimize agent-generated SQL queries through partition pruning, file layout awareness, query guardrails, and distributed query performance techniques.
- Design, deploy, and maintain self-hosted AI/ML model-serving environments using Kubernetes, including deployments within disconnected or restricted network environments.
- Develop and orchestrate automated model training, evaluation, and agent workflows using Apache Airflow, AWS services, and Kubernetes.
- Leverage AWS technologies, including Amazon S3, EC2, and EMR, to support distributed AI/ML processing and deployment.
- Evaluate LLM and AI agent outputs using structured test datasets, evaluation rubrics, regression testing, and performance metrics.
- Monitor AI/ML solutions for model drift, performance degradation, and operational failure modes.
- Collaborate with data engineers, data scientists, and cloud/platform engineers to integrate AI/ML models into production data pipelines and enterprise environments.
- Apply responsible AI principles, including bias assessment, model transparency, data handling, and risk management.
- Provide technical leadership, mentor junior engineers, and contribute to engineering best practices.
- Develop and maintain technical documentation, architecture designs, operational runbooks, and implementation guidance.
- Communicate model behavior, limitations, and technical risks to technical and nontechnical stakeholders.
Qualifications & Required Skills
- Active U.S. Government Secret security clearance.
- U.S. Citizenship required.
- Minimum of 8 years of professional experience in software engineering, data engineering, or machine learning, including at least 4 years of applied AI/ML experience.
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field. Additional relevant professional experience may substitute for education on a year-for-year basis.
- Demonstrated experience developing and deploying distributed machine learning solutions using Apache Spark, including Spark MLlib, and lakehouse table formats such as Apache Iceberg.
- Strong statistical background, including probability, statistical inference, regression analysis, experimental design, and model evaluation.
- Experience applying classical machine learning algorithms and techniques to large-scale datasets.
- Hands-on experience designing and developing agentic AI workflows involving tool calling, MCP, and multi-step AI agents.
- Experience working with LLMs, retrieval-augmented generation (RAG), and vector databases.
- Strong understanding of distributed SQL query engines, including Amazon Athena, Trino, and Spark SQL, with the ability to optimize AI-generated queries.
- Experience deploying and serving AI/ML models in self-hosted or disconnected environments.
- Strong programming experience with Python.
- Hands-on experience with Apache Airflow for workflow orchestration.
- Experience with AWS cloud services, including S3, EC2, and EMR.
- Experience deploying and managing applications or AI/ML workloads using Kubernetes.
- Strong analytical, critical-thinking, and problem-solving skills.
- Demonstrated ability to collaborate with multidisciplinary technical teams to deploy and operationalize AI/ML solutions.
- Ability to explain complex AI/ML concepts, model behavior, limitations, and technical risks to nontechnical stakeholders.
- Experience providing technical leadership and mentoring junior engineers.
- Understanding of responsible AI practices, including bias, model evaluation, and secure data handling.
Preferred Qualifications & Skills
- Master's degree or Ph.D. in Statistics, Computer Science, or a related quantitative field.
- Experience developing MCP servers or working with established AI agent frameworks.
- Experience implementing LLM evaluation frameworks, automated testing methodologies, and AI guardrails.
- Advanced knowledge of Apache Iceberg architecture and internals.
- Experience working with AWS Glue Data Catalog.
- Experience with Terraform and Infrastructure as Code (IaC).
- Prior experience supporting federal or DoD AI/ML programs.
- Experience designing and implementing scalable AI/ML solutions in secure, highly regulated environments.
Benefits
- Comprehensive Medical, Dental, and Vision Plans
- Life Insurance
- Paid Time Off (Flexible/Combined PTO, Bereavement Leave, 11 Company Paid Holidays)
- 401K Retirement Plan with employer match
- Professional Development Training Reimbursement
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At VivSoft technologies, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
VivSoft technologies AI Hiring
VivSoft technologies has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, Springfield, VA, US. Compensation range: $145K - $145K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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