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About This Role
Role Overview:
- Client is seeking a Forward Deployed AI Engineer to join the AI Foundry team and work directly with customers to deploy and operationalize AI\-powered workflow solutions.
- This role blends full\-stack engineering, AI implementation, and client\-facing solution delivery. Forward Deployed AI Engineers partner closely with business consultants, product teams, and customer stakeholders to translate real\-world problems into deployable AI\-driven solutions.
- You will help customers implement agentic AI workflows, intelligent automations, and AI\-powered integrations within ServiceNow and enterprise ecosystems, while also contributing to the evolution of client’s AI platforms, accelerators, and intellectual property, including the client Intelligence Platform, Value Realization Dashboard, and Data Intelligence Platform.
- This role requires strong engineering skills, curiosity about emerging AI technologies, and the ability to operate effectively in fast\-moving customer environments.
Key Responsibilities:
Client Delivery \& AI Solution Implementation
- Deploy and configure agentic AI workflows and AI\-powered automations within client ServiceNow environments.
- Translate customer business requirements into technical architectures and implementation plans.
- Implement and integrate NewRocket Agent Packs and AI solutions into enterprise environments.
- Work directly with customers to tailor AI solutions to their operational workflows and business processes.
Solution Engineering \& Prototyping
- Build demos, prototypes, and proof\-of\-concept implementations to validate AI\-driven workflows with clients.
- Rapidly iterate solutions with customers to refine AI\-powered use cases.
- Support implementation of AI orchestration, LLM integrations, and agentic decision models.
Full Stack Engineering \& Integration
- Develop integrations between ServiceNow, enterprise systems, APIs, and AI services.
- Build supporting components such as scripts, microservices, automation logic, and integration services.
- Implement integrations with AI platforms, APIs, and enterprise data sources.
Product \& Platform Contribution
Actively contribute to the development and evolution of NewRocket’s AI intellectual property and platforms, including:
- NewRocket Intelligence Platform
- Value Realization Dashboard
- Data Intelligence Platform
- Agent Packs and reusable AI solution accelerators
Responsibilities include:
- identifying patterns and capabilities discovered in client deployments
- contributing reusable assets and automation components
- providing product feedback that improves platform capabilities
- helping transform successful client implementations into repeatable platform features
- Client Interaction \& Technical Consulting
- Participate in workshops, discovery sessions, and technical working sessions with customers.
- Serve as the engineering counterpart to consulting and delivery teams.
- Communicate architecture, system behavior, and technical tradeoffs clearly to stakeholders.
- Systems Integration \& Troubleshooting
- Diagnose and resolve technical issues across AI workflows, integrations, and automation pipelines.
- Ensure deployed AI solutions are secure, scalable, and production\-ready.
- Optimize deployed systems for performance and reliability.
- Cross\-Team Collaboration
Work closely with:
- Business Process Consultants
- Product Engineering
- Data Engineers
- AI / ML Engineers
- AI Center of Excellence teams
- Contribute to internal playbooks, reusable deployment patterns, and product evolution.
What Success Looks Like (First 6 Months)
- Successfully deploy AI\-powered solutions across multiple client engagements.
- Deliver working AI workflows and integrations within customer ServiceNow environments.
- Build trusted relationships with customer teams and internal delivery teams.
- Contribute reusable components and improvements to NewRocket’s AI platforms and accelerators.
- Provide actionable feedback that improves the NewRocket Intelligence Platform and related products.
Required Qualifications
- 5–8\+ years of experience in software engineering, systems integration, enterprise platforms, or automation systems.
- Strong engineering foundation with experience in full\-stack development, scripting, or enterprise integrations.
- Experience working with AI / LLM technologies or AI\-powered applications.
- Experience operating in client\-facing engineering or consulting roles.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience with languages such as:
+ JavaScript / TypeScript
+ Python
+ or similar scripting languages.
- Strong ability to communicate complex technical concepts to both technical and business stakeholders.
Preferred Qualifications
- ServiceNow Experience (Strong Plus)
- Experience with ServiceNow development or workflow automation platforms.
- Familiarity with ServiceNow scripting, APIs, and platform development patterns.
- Understanding of ServiceNow data models, including:
+ CMDB
+ workflow / task tables
+ knowledge management
+ integrations with enterprise systems.
- AI \& Platform Experience
- Experience building AI prototypes, RAG systems, or agent\-based workflows.
- Experience integrating LLM APIs or AI services into enterprise systems.
- Experience with vector databases, embeddings, or semantic retrieval.
- Experience contributing to internal platforms, reusable accelerators, or product capabilities.
Why This Role Matters
- Forward Deployed AI Engineers serve as the bridge between real\-world enterprise problems and NewRocket’s AI platforms. By working directly with customers, they help deliver immediate value while also shaping the evolution of NewRocket’s AI products, accelerators, and platform capabilities.
- This role is critical to scaling the NewRocket Intelligence Platform and AI Foundry strategy.
Benefits at IntelliBee
- Long\-Term Stability: Join us on a multi\-year opportunities with room to grow.
- Comprehensive Health Coverage: Access quality healthcare benefits to keep you and your family well.
- Future Planning: Enroll in our 401(k) program and invest in your financial security.
- GC Assistance: We support immediate Green Card processing, if required.
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 Intellibee 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Intellibee Inc AI Hiring
Intellibee Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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
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