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About This Role
We are hiring an Agentic Solutions Architect to design production-grade agentic solutions on our internal platform across multiple delivery squads. This role connects business needs, product intent, and engineering execution: you will understand the problem to solve, shape viable options, make and communicate tradeoffs, be hands on keyboard coding, manage a team of engineers, and guide squads toward secure, observable, reusable, and supportable outcomes.
You will architect across several squads rather than serving as the architect for a single use case. You will partner with Product, Program, Tech Leads, engineers, security, and business stakeholders to ensure solution architectures align with the platform standards and broader engineering strategy.
What You'll Do
- Partner with team leadership and business stakeholders to understand goals, workflows, users, constraints, and success measures.
- Help determine whether an agentic solution is appropriate, or whether a deterministic workflow, existing capability, or no-AI solution is better.
- Be able to challenge the status quo and look for simplicity, scalability, and staged solutions that deliver incremental value when needed.
- Translate business needs into technical requirements, scope boundaries, delivery roadmaps, architectural options, measurable outcomes, and implementation plans.
- Explain technical implications and communicate tradeoffs across scope, cost, quality, latency, security, risk, and delivery speed.
- Own solution architecture for a portfolio of use cases and agentic applications delivered by several squads within a larger delivery organization.
- Map end-to-end workflows, trigger events, human approvals, system interactions, failure paths, and operational handoffs.
- Define agent package structure, task graphs, orchestration patterns, and the division of responsibility between agents, deterministic code, tools, and platform services.
Identify required systems, data sources, outputs, APIs, integrations, tools, and MCP capabilities.
- Work with partners to ensure dependencies are accounted for and managed across the portfolio of solutions in the role’s purview.
- Produce architecture diagrams, decision records, implementation guidance, acceptance criteria, and nonfunctional requirements.
- Manage cross-squad dependencies, technical debt, duplicate capabilities, and opportunities for reusable patterns, templates, connectors, and reference implementations.
- Collaborate with delivery teams to translate architecture into work packages, estimates, quality criteria, and delivery plans.
- Be hands on keyboard to guide and work with the teams and understand solutions to a granular level.
- Apply architecture standards, approval controls, guardrails, and governance requirements throughout delivery.
- Work with leadership to maintain solution architecture across teams for technical direction, engineering standards, capacity, and risk posture.
- Support developer onboarding and architecture education across squads.
- Coordinate decisions and dependencies across product, engineering, security, data, integrations, and business teams.
- Communicate architecture status, risks, dependencies, and tradeoffs to technical and executive audiences.
- Directly manage engineers executing across multiple small, focused product squads.
What You'll Bring
Required experience
- AWS and cloud-hosted foundation models or agent platforms, such as Amazon Bedrock or AgentCore, or equivalent technologies.
- Python, TypeScript/JavaScript, REST or GraphQL APIs, event-driven systems, serverless or containerized workloads, and infrastructure as code.
- Significant experience in software architecture, solutions architecture, technical leadership, or a similar role designing and delivering enterprise systems.
- Experience architecting and shipping production AI, generative AI, agentic, conversational, or intelligent-automation solutions.
- Strong understanding of agentic patterns, including orchestration, tool use, RAG, model selection, prompt and policy controls, human-in-the-loop approval, state and memory, evaluation, and fallback behavior.
- Experience integrating enterprise systems through APIs, events, connectors, workflow engines, or MCP-style tool patterns.
- Strong understanding of identity, authorization, data access, privacy, auditability, and secure-by-default design.
- Demonstrated ability to influence multiple teams or squads without relying solely on formal authority.
- Excellent written and verbal communication, including the ability to explain complex architecture and tradeoffs to business stakeholders and executives.
- Experience creating architecture diagrams, ADRs, technical requirements, nonfunctional requirements, and implementation guidance.
Preferred experience
- Experience with enterprise SaaS and business systems such as Salesforce, ServiceNow, Microsoft 365, SharePoint, or similar platforms.
- Establishing reusable architecture standards, reference architectures, developer patterns, or platform governance.
- Leading architecture reviews involving security, compliance, privacy, or production risk.
- Relevant cloud, security, AI, or architecture certifications. Certifications are helpful but are not a substitute for demonstrated production experience.
- Experience coordinating and managing the work of engineers against a timeline.
What Success Looks Like
- Multiple squads can move independently while following coherent architecture patterns and shared platform standards.
- Business stakeholders understand solution options, key tradeoffs, and expected outcomes before significant implementation investment.
- Each solution has clear boundaries, documented decisions, nonfunctional requirements, security controls, operational ownership, and a credible path to production.
- Reusable capabilities are adopted across squads instead of repeatedly rebuilt as bespoke solutions where possible.
- Risks, dependencies, exceptions, and technical debt are visible early and actively managed.
- Solutions deliver measurable business value while meeting expectations for quality, safety, reliability, observability, and cost.
- Squad leaders share a clear view of how use-case architecture supports platform engineering goals.
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-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ahead AI Hiring
Ahead has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $180K - $230K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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