Interested in this AI/ML Engineer role at Ahead?
Apply Now →Skills & Technologies
About This Role
AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.
At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.
We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.
*We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.*
As a Senior Technical Consultant focused on AI capabilities, you will own the end‑to‑end build of AI‑enabled solutions on the Now Platform — Now Assist skills and AI Agents through predictive models, AI Search, and the data foundations that make them work. You will guide development activities, mentor technical consultants and junior developers, and partner with Principal consultants and architects to shape complex agentic solutions. Beyond core platform development, you will lead AI enablement across at least one additional product suite (ITSM, ITOM, ITAM, SecOps, IRM, CSM, HRSD, SPM, or ESM) and translate ambiguous business outcomes into secure, governed, measurable AI capabilities. Your depth in both platform engineering and applied AI will influence how our clients adopt agentic workflows and how they realize value from them.
### Roles \& Responsibilities
Solution and Stakeholder Leadership
- Translate business outcomes and documented requirements into AI solutions that are secure, governed, explainable, and aligned to platform best practices
- Identify and qualify AI use cases with clients, assessing data readiness, deflection or cycle‑time potential, risk tolerance, and human‑in‑the‑loop requirements. Articulate plainly when a use case is a poor fit for AI.
- Conduct client and internal demos of Now Assist, AI Agents, AI Control Tower and agentic workflows, clearly explaining how outputs are produced, where guardrails sit, and what the measured impact is
- Actively participate in Agile ceremonies, flagging technical and AI‑specific risks (data quality, hallucination exposure, adoption drag, licensing consumption) during planning
Hands‑On Development and Delivery Governance
- Build and extend Now Assist skills, AI Agents, agentic workflows, and orchestration logic; author and tune prompts, tool definitions, and agent instructions against defined success criteria
- Develop the supporting platform foundation: integrations, Flow Designer and Integration Hub actions, custom tools exposed to agents, Knowledge and catalog data quality, and the taxonomy that AI Search and Now Assist depend on
- Configure and tune Predictive Intelligence models, Document and Task Intelligence, Virtual Agent and NLU/Conversational Interfaces, and AI Search relevancy
- Extend AI beyond native capabilities via Generative AI Controller, AI Agent Fabric / MCP, and third‑party LLM or agent integrations where the use case warrants it
- Establish evaluation discipline: baseline metrics, golden datasets, regression test suites for prompts and skills, A/B and pre/post measurement, and drift monitoring after go‑live
- Enforce responsible‑AI guardrails — data handling and PII scoping, role‑based access to AI capabilities, audit and trace requirements, human approval gates, and configuration in AI Control Tower
- Safeguard quality through peer reviews, automated tests, and coordinated promotions across dev, test, and prod, including cutover and rollback strategies for AI features
- Own defect resolution during UAT and hyper‑care, including model and prompt performance issues, driving root‑cause analysis and continuous tuning
Team Leadership and Mentoring
- Coach junior developers on AI fundamentals, prompt and agent design patterns, and the judgment to distinguish a demo from a production‑ready solution
- Coordinate daily development tasks, remove roadblocks, and safeguard delivery timelines
- Facilitate training sessions and knowledge‑sharing forums that raise AI fluency across the broader delivery team
Innovation and Cross‑Product Leadership
- Lead AI delivery across at least one product suite beyond core platform work, understanding the process being augmented well enough to know where AI genuinely helps
- Build reusable accelerators — skill libraries, agent patterns, evaluation harnesses, readiness assessments — and drive their adoption across engagements
- Track each ServiceNow release for new AI capabilities, evaluate them hands‑on, and advise clients on adoption sequencing and licensing implications
- Contribute lessons learned, benchmarks, and technical articles to internal knowledge bases and external community forums
### Qualifications
- 6\+ years in the ServiceNow domain, with meaningful recent time spent building AI‑enabled solutions in production
- ServiceNow AI depth – Now Assist, AI Agent Studio and AI Agent Orchestrator, Now Assist Skill Kit, AI Search, Predictive Intelligence, Document/Task Intelligence, Virtual Agent and NLU, AI Control Tower, and Generative AI Controller
- Data foundation fluency – Understands that AI outcomes track data quality; comfortable with Workflow Data Fabric, CMDB/CSDM health, knowledge governance, and taxonomy design as prerequisites rather than afterthoughts
- Core‑platform expertise – Integrations, Integration Hub, Flow Designer, Service Portal, UI Builder and Workspaces, imports, plus an architecture mindset for performance, scalability, and clean upgrades
- Hands‑on coding – Advanced JavaScript and Glide APIs, REST integration design and consumption, auth schemes, and data pipelines; strong vanilla JavaScript fundamentals with testing habits and version‑control discipline
- Applied AI craft – Prompt engineering and iteration, retrieval and grounding patterns, tool/function calling, agent decomposition and orchestration, and a working grasp of where LLMs fail and how to contain it
- Evaluation and measurement rigor – Defines success metrics before building, tests systematically, and reports honest results including negative ones
- Responsible AI judgment – Practical command of data privacy, access control, auditability, bias and hallucination risk, and the governance conversations that come with them
- Product depth – Proven leadership in at least one suite beyond core ITSM and Service Portal
- Collaborative mentor and lifelong learner – Explains AI concepts simply to non‑technical stakeholders, calibrates expectations against hype, and stays current in a space that changes quarterly
- ServiceNow certifications – CSA, CAD, CIS, and AI‑related micro‑certifications are welcome, though demonstrated hands‑on expertise is valued more highly than credentials
- Broader tech stack awareness – Familiarity with LLM providers and APIs, vector search and RAG architectures, MCP, cloud platforms, DevOps toolchains, or analytics outside the ServiceNow ecosystem
*The compensation range indicated in this posting reflects the On\-Target Earnings (“OTE”) for this role, which includes a base salary and any applicable target bonus amount. This OTE range may vary based on the candidate’s relevant experience, qualifications, and geographic location.*
Why AHEAD:
Through our daily work and internal groups like Moving Women AHEAD and RISE AHEAD, we value and benefit from diversity of people, ideas, experience, and everything in between.
We fuel growth by stacking our office with top\-notch technologies in a multi\-million\-dollar lab, by encouraging cross department training and development, sponsoring certifications and credentials for continued learning.
USA Employment Benefits include:
- Medical, Dental, and Vision Insurance
- 401(k)
- Paid company holidays
- Paid time off
- Paid parental and caregiver leave
- Plus more! See benefits https://www.aheadbenefits.com/ for additional details.
Use of AI:
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, assessing responses, or to capture recordings and create transcriptions or summaries during interviews. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans.
If you would like more information about how your data is processed, please refer to the Candidate Privacy Notice or contact us at [email protected].
You may opt\-out of the review or analysis of your application and resume by AI tools by using the General Application. Please include the role you wish to apply for in the Additional Information field. You may also choose to opt\-out of recording and transcription at any time, including after joining an interview. Candidates will not be penalized for choosing to opt\-out.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $160K-$190K range is above 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 Ahead, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $160K to $190K.
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.
Ahead AI Hiring
Ahead has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, US. Compensation range: $150K - $300K.
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.