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About This Role
NUWAVE POSITION SUMMARY
NUWAVE is a market leader in unified communications as a service and enterprise voice for Microsoft Teams.
We deliver carrier-grade communications to enterprises and service providers worldwide. Our platforms are
backed by BSI-certified management systems, including ISO/IEC 42001 for AI governance.
We are looking for an AI Engineer for a full-time, U.S.-remote role helping build and operate Nebula,
NUWAVE's governed conversational AI workspace. Nebula combines a GraphRAG knowledge engine, business
applications, and agent workflows across the web and Microsoft Teams.
This role works closely with the Director of AI Infrastructure to turn Jira issues into verified production
changes. You will use AI coding agents as part of the daily engineering workflow while remaining accountable
for system design, code review, testing, deployment discipline, and operational judgment. This is a hands-on
engineering role, not a prompt-writing position.
Why Join NUWAVE
You will inherit a working AI-first engineering system and receive direct mentorship. As you grow, you will
take ownership of significant Nebula workstreams and help shape how NUWAVE builds and operates AI
products.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Build, test, and maintain AI applications, cloud services, and supporting infrastructure for Nebula.
- Translate Jira issues and product requirements into scoped technical changes with clear acceptance criteria.
- Use AI coding agents to accelerate implementation while reviewing their output for correctness, security, and
maintainability.
- Develop and debug Python services and contribute to TypeScript applications when needed.
- Operate workloads across AWS and Azure, including serverless, container, identity, data, and observability services.
- Work with retrieval-augmented generation, GraphRAG, vector search, graph databases, and agent workflows.
- Write infrastructure as code and follow disciplined source-control, review, release, and rollback practices.
- Verify changes through automated tests, logs, metrics, and direct functional checks before considering work
complete.
- Document designs, operational procedures, decisions, and status clearly enough for others to act without a
meeting.
- Participate in production support, incident response, and occasional after-hours work when required.
OTHER DUTIES AND RESPONSIBILITIES
Some on-call work, including weekends or holidays, may be required.
SUPERVISORY RESPONSIBILITIES
None.
MINIMUM QUALIFICATIONS KNOWLEDGE SKILLS AND ABILITIES
- Strong software engineering fundamentals and working fluency in Python, including the ability to read, debug,
and review unfamiliar code.
- Hands-on experience with at least one major cloud platform, preferably AWS or Azure.
- Practical experience using AI coding tools such as Claude Code, Codex, Cursor, or similar agentic tools.
- Demonstrated ownership: able to turn a goal into a plan, identify blockers early, and carry work through
verification.
- Sound Git, testing, deployment, and operational discipline, with clear evidence of what changed and how it
was validated.
- Clear written and verbal communication with technical and nontechnical partners.
Preferred Qualifications
- Experience with LangGraph, LangChain, RAG or GraphRAG systems, vector search, or graph databases.
- Experience with Terraform, OpenTofu, or another infrastructure-as-code framework.
- Experience building on Microsoft 365 or Teams, including Microsoft Graph, Teams apps, or Microsoft Entra ID.
- Exposure to multi-agent orchestration patterns or development of internal agent tooling.
- Security-minded engineering and familiarity with formal management systems such as ISO/IEC 42001.
- Experience with TypeScript, containerized services, serverless systems, or distributed systems.
PHYSICAL DEMANDS AND WORK CONDITIONS AND ENVIRONMENT
None.
EQUIPMENT NECESSARY TO COMPLETE JOB RESPONSIBILITIES
Desk Phone Yes No
Mobile Phone Yes No
Computer or Laptop Yes No
Internet Connection Yes No
NOTE
This job description does not state or imply that these are the only duties of this position. Employees must
follow other job-related instructions and perform other job-related duties assigned by authorized persons.
Duties and responsibilities may be modified to reasonably accommodate individuals with disabilities. The
employee must possess the skills, aptitudes, and abilities to perform each duty proficiently. Requirements
may exclude individuals who pose a direct threat or significant risk to their own health or safety or that of
others. The requirements listed are the minimum levels of knowledge, skills, or abilities. This document does
not create an employment contract, implied or otherwise, other than an at-will relationship.
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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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Nuwave Communications, 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 $212,000 based on 11,866 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 $213,380. 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.
Nuwave Communications, Inc. AI Hiring
Nuwave Communications, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 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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