Interested in this AI/ML Engineer role at DATA PULSE TECH AI LLC?
Apply Now →Skills & Technologies
About This Role
U.S. citizenship is required by the federal engagement supporting this role. Candidates must reside in and perform all work from within the United States. Work is remote, although occasional approved travel may be required. Visa sponsorship is not available.
Lanthos is hiring a hands\-on GCP Generative AI Application Engineer to build and support applications using Gemini models through Vertex AI for a regulated public\-sector program.
This is a junior\-to\-mid\-level individual\-contributor role. You will implement, test, monitor and document AI application features within established cloud, security and architecture patterns. You will work alongside senior technical leads and will not be expected to own the program’s enterprise architecture.
What you’ll do
- Build and test Python services that integrate Gemini models and other approved models through Vertex AI.
- Implement retrieval\-augmented generation workflows, including ingestion, chunking, embeddings, retrieval, citations and grounded responses.
- Develop containerized APIs and application components and deploy them to Cloud Run or another approved managed runtime.
- Connect services to approved sources such as Cloud Storage, BigQuery, enterprise search indexes and internal APIs.
- Create automated tests and evaluation checks for retrieval quality, groundedness, failure handling and response consistency.
- Add application logging, monitoring and useful operational diagnostics.
- Use Git\-based development, code review and CI/CD practices.
- Maintain implementation notes, test evidence and runbooks for security review and operational handoff.
Minimum qualifications
- Two or more years of relevant software, data or machine\-learning engineering experience, including at least one year of hands\-on Google Cloud work.
- Strong practical Python skills, including APIs, testing, error handling and data processing.
- Hands\-on experience personally building an LLM\-enabled or RAG application with Gemini on Vertex AI beyond prompt\-only experimentation.
- Experience deploying a containerized application or service on Google Cloud.
- Familiarity with Git, REST APIs and Docker.
- A current Google Cloud Associate Cloud Engineer, Professional Cloud Developer or Professional Machine Learning Engineer certification.
- Ability to explain personal technical contributions clearly and maintain useful documentation.
Preferred qualifications
- Hands\-on experience with Gemini on Vertex AI, Vertex AI Search or Vertex AI Vector Search.
- Experience with Cloud Run, Cloud Storage or BigQuery.
- Familiarity with embeddings, retrieval evaluation and LLM response\-quality testing.
- Familiarity with Google Agent Development Kit or another agent orchestration framework.
- Experience using GitHub Actions, Cloud Build or another CI/CD platform.
- Experience delivering software in a federal, public\-sector or regulated environment.
- Google Cloud Professional Cloud Developer or Professional Machine Learning Engineer certification.
Federal program requirements
- Must be a U.S. citizen.
- Must reside in and perform all work from within the United States.
- Must be able to obtain and maintain the required MBI/Public Trust suitability determination.
- Must be willing to complete required fingerprinting, identity verification, security and privacy training and applicable compliance checks.
Compensation and engagement
- $70\.00–$90\.00 per hour, based on relevant experience and final assigned scope.
- Independent\-contractor engagement, anticipated up to 40 hours per week based on project needs and approved work.
- Direct engagement with the individual or a candidate\-owned corporation may be considered.
- This contract engagement does not include company\-sponsored employee benefits. No bonus, commission or equity compensation is currently offered.
- Third\-party staffing submissions are not accepted.
- Applications are expected to remain open through August 21, 2026\. The deadline may be extended if the position remains open.
Interview process
- Initial conversation covering eligibility, availability and engagement expectations.
- Practical technical interview focused on Python, cloud application delivery and an AI or RAG project the applicant personally implemented.
- Team conversation covering communication, collaboration and regulated\-program fit.
Lanthos LLC is an equal opportunity employer. The citizenship and suitability requirements above are imposed by the federal engagement supporting this position.
Pay: $70\.00 \- $90\.00 per hour
Application Question(s):
- Do you meet the U.S.\-citizenship requirement stated for this federal engagement? (Yes/No)
- Are you currently located in the United States and able to perform all work from within the United States? (Yes/No)
- Are you willing and able to complete an MBI/Public Trust suitability process, including fingerprinting and required compliance checks? (Yes/No)
- Are you available for a contract engagement anticipated up to 40 hours per week and comfortable proceeding within the posted $70–$90/hour range? (Yes/No)
- How many years of professional Python software\-development experience do you have? Enter a number.
- Do you currently hold an active Google Cloud Associate Cloud Engineer, Professional Cloud Developer or Professional Machine Learning Engineer certification? (Yes/No)
- Provide the certification name, expiration date and a public credential link or credential ID.
- Which statement best describes your hands\-on Gemini\-on\-Vertex\-AI application experience? Choose one: production application; working pilot/internal application; personal project/coursework only; or no hands\-on experience.
- In no more than 150 words, describe one Gemini\-on\-Vertex\-AI application you personally built. Include your contribution, GCP deployment environment and how you tested retrieval or response quality.
Work Location: Remote
Salary Context
This $145K-$187K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At DATA PULSE TECH AI LLC, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($166K) sits 23% below the category median. Disclosed range: $145K to $187K.
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.
DATA PULSE TECH AI LLC AI Hiring
DATA PULSE TECH AI LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $187K - $312K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.