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About This Role
Description:
Rate: $80 \- $90/ hour (Depending on Experience)
*Note: MUST be US Citizen or Green Card Holder*
\*\*\*NO RECRUITING AGENCIES\*\*\*
\*\*\*NO C2C\*\*\*
\*\*\*NO Sponsorship available\*\*\*
About e360’s App Engineering
e360 is a 30\+ year privately\-owned company with a focus on our people, our clients and leading technologies. e360’s Cloud Services Division is a rapidly growing business helping clients manage their Cloud technology. Our team is comprised of leaders that focus on delivering innovative consulting solutions that leverage leading and emerging technologies.
We are a dynamic and entrepreneurial consulting company that offers ample opportunities for professional development and growth suited to each individual’s personal and professional goals. We offer internal, and subsidize external, trainings, and reimburse the cost of technology certification exams and / or renewals. Our family\-founded business sees work life fit as a core value that all of our practitioners practice – the value you add to your team is more important than the time that you ‘clock in and out.’ You will have numerous opportunities to interface with senior leadership, and benefit from mentorship internally or through introductions through external networks to support your growth.
Description
The Advanced Generative AI Developer is a hands\-on consultant responsible for designing, building, and deploying production\-ready Generative AI and agentic solutions on Google Cloud.
This role requires strong Python and cloud development experience, practical knowledge of Google Agent Development Kit, Gemini, Vertex AI, and GCP\-native application and data services. The consultant will work directly with client and project teams to translate business requirements into secure, scalable, and maintainable AI solutions.
What You’ll Do
- Design, build, test, and deploy Generative AI applications and intelligent agents on Google Cloud.
- Develop single\-agent and multi\-agent solutions using Google Agent Development Kit.
- Integrate Gemini models with enterprise APIs, databases, applications, and business workflows.
- Deploy AI applications using Agent Engine, Cloud Run, GKE, or other appropriate GCP services.
- Build Retrieval\-Augmented Generation solutions using services such as BigQuery, Vertex AI Vector Search, Cloud Storage, and Document AI.
- Develop APIs, microservices, agent tools, MCP integrations, and event\-driven workflows.
- Build data pipelines to ingest, transform, chunk, embed, index, and retrieve structured and unstructured data.
- Implement session management, memory, tool calling, human approval, and agent orchestration patterns.
- Apply automated testing, CI/CD, logging, monitoring, tracing, evaluation, and cost\-management practices.
- Implement Google Cloud security using IAM, service accounts, Workload Identity Federation, Secret Manager, and private networking.
- Troubleshoot issues across agents, models, APIs, data pipelines, integrations, security, and cloud deployments.
- Create architecture diagrams, technical designs, API specifications, deployment guides, and operational documentation.
- Own technical workstreams and provide design reviews, code reviews, and guidance to other developers.
- Participate in client discovery, architecture, testing, deployment, and knowledge\-transfer activities.
Requirements:
- Significant experience developing and deploying applications on Google Cloud.
- Advanced Python development experience.
- Hands\-on experience building Generative AI or agentic applications.
- Experience with Google Agent Development Kit, including agents, tools, workflows, sessions, state, and multi\-agent patterns.
- Experience integrating Gemini models using Vertex AI or Google Gen AI SDKs.
- Experience with Agent Engine, Cloud Run, GKE, Cloud Functions, or similar GCP runtimes.
- Experience designing and implementing RAG solutions.
- Experience with BigQuery and Google Cloud data services.
- Experience building APIs using frameworks such as FastAPI.
- Experience with REST APIs, asynchronous processing, event\-driven architecture, and microservices.
- Understanding of MCP and its use in connecting agents to enterprise tools and systems.
- Experience with SQL, document stores, object storage, embeddings, semantic search, or vector databases.
- Experience with Git, automated testing, CI/CD, Docker, and infrastructure as code.
- Understanding of Google Cloud IAM, service accounts, Secret Manager, networking, logging, and monitoring.
- Ability to evaluate tradeoffs involving model quality, latency, security, scalability, reliability, and cost.
Candidates are not expected to have experience with every listed GCP service. However, they must have hands\-on experience delivering Generative AI solutions and be able to explain their architecture and implementation decisions.
Preferred Qualifications
- Experience delivering client\-facing Google Cloud consulting projects.
- Experience leading a technical workstream from discovery through production deployment.
- Experience deploying ADK agents using Agent Engine, Cloud Run, or GKE.
- Experience implementing MCP servers, custom agent tools, or enterprise integrations.
- Experience with Vertex AI Vector Search, BigQuery Vector Search, Document AI, Apigee, Pub/Sub, Eventarc, or Workflows.
- Experience with Terraform, Cloud Build, Artifact Registry, and automated GCP deployment pipelines.
- Experience implementing AI evaluation, agent testing, observability, guardrails, and cost monitoring.
- Relevant Google Cloud certifications.
Professional Skills
- Strong consulting, communication, and problem\-solving skills.
- Ability to translate business requirements into practical technical solutions.
- Ability to explain complex AI and cloud concepts to technical and non\-technical stakeholders.
- Strong documentation and technical leadership skills.
- Ability to work independently and manage changing project priorities.
- Ability to identify and communicate technical risks, dependencies, and blockers.
- Willingness to mentor other developers and contribute to reusable delivery standards.
Critical Success Factors
- Ability to independently design and deliver production\-ready AI solutions on Google Cloud.
- Strong practical knowledge of Google ADK, Gemini, Vertex AI, and GCP architecture.
- Ability to build agents that securely interact with APIs, data, tools, and enterprise systems.
- Ability to determine when to use agentic, deterministic, serverless, containerized, or managed\-service patterns.
- Commitment to security, testing, observability, governance, maintainability, and cost control.
- Ability to own technical workstreams and consistently deliver high\-quality client outcomes.
Salary Context
This $166K-$187K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Entisys 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $166K to $187K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Entisys Solutions AI Hiring
Entisys Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US. Compensation range: $187K - $187K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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