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About This Role
Dark Wolf is seeking a Data & AI Engineer specializing in Google Cloud Platform to architect, deploy, and scale advanced data, machine learning (ML) and generative artificial intelligence (AI) workflows in secure cloud environments. In this role, you will be responsible for leading the architectural development, scaling, and governance of enterprise-grade data platforms. In this role you will leverage GCP's AI and data ecosystem to transform complex unstructured data into production-ready assets for agentic search, graph-based entity resolution, and multi-modal AI systems. In addition, the ideal candidate will be responsible for designing secure, mission-relevant data meshes, establishing automated data quality frameworks, optimizing distributed compute clusters, and ensuring high resilience across air-gapped and multi-cloud networks.
Responsibilities:
- Lead the architectural design and implementation of cloud-native distributed, high-throughput batch and streaming data platforms.
- Optimize distributed execution engines to handle petabyte-scale datasets under strict SLAs.
- Implement and collaborate on Infrastructure as Code (IaC) to provision data platform assets across multi-cloud infrastructure.
- Establish zero-trust security controls, data lineage tools, and automated compliance scanning for regulated data environments.
- Design and manage ETL/ELT pipelines using cloud-native, Google services such as Cloud Storage, Dataflow, Cloud Run, Agent Platform, and BigQuery
- Understand big data processing frameworks for large-scale data sets such as Apache Spark.
- Architect and process large-scale datasets and cloud data warehouses using tools such as Google BigQuery and Knowledge Catalog.
- Programming experience to build automation scripts, handle data structures and connect to APIs.
- Configure and manage GCP services such as Compute Engine, App Engine, Kubernetes Engine, Cloud Storage, BigQuery, Dataflow, and Cloud SQL.
- Enforce cloud security best practices and support DoD/DISA Authority to Operate (ATO) processes.
- Act as Technical Pod Lead, driving Agile spring deliverables, establishing technical standards, and mentoring data engineering staff.
- Stay up-to-date on the latest GCP services and technologies.
- Work collaboratively within a fast paced Agile team environment.
Qualifications:
- Bachelor's degree in computer science or a related field (or equivalent practical experience)
- 4+ years of data engineering experience.
- 4+ years of experience in architecting data structures for model grounding and engineering pipelines that transform raw data into tool-accessible assets for agentic workflows.
- 4+ years of experience with Python, Scala, SQL, and Go for high-performance data platform development.
- 4+ years of experience with distributed systems internals, LMS trees, column-family stores, and data warehouse and lakehouse storage formats.
- 4+ years of experience with Kubernetes cluster management, custom operators for data platforms, and service mesh integration.
- 4+ years of experience with enterprise data strategy, conceptual-to-physical data modeling, and streaming data schema contracts.
- 4+ years of experience on GCP supporting AI/ML research, experimentation, and production delivery.
- Experience and expertise in Apache Spark, Flink, Kafka, Ray, and workflow orchestration engines.
- Experience architecting multi-cloud and hybrid cloud data platform solutions (IaC via Terraform).
- Understanding of DevSecOps pipelines, automated compliance testing, and secret rotation management .
- Strong understanding of GCP services and technologies.
- Excellent communication and teamwork skills.
- Must be a U.S. Citizen holding an active Secret Security Clearance.
Preferred Qualifications:
- Active Google Cloud Professional Certification (e.g., Data Engineer, ML Engineer, Cloud Architect) or CISSP.
- Proven track record architecting data solutions for deployment in air-gapped or high-assurance defense networks.
- Experience with GCP security/compliance products (Security Command Center, Assured Workloads) and DoD/DISA cybersecurity policies.
This position will be hybrid / remote based out of a Dark Wolf Hub: Herndon VA, Tampa FL, Colorado Springs CO, Ogden UT, or Omaha NE.
The salary range for this position is estimated to be the following, commensurate on experience and technical skillset.
- Mid Level: $120,000.00 - $160,000.00
- Senior Level: $160,000.00 - $190,000.00
We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.
Salary Context
This $120K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Dark Wolf 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $120K to $190K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Dark Wolf Solutions AI Hiring
Dark Wolf Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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