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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.*
The Forward Deployed Engineer is responsible for deploying, integrating, and operationalizing next\-generation security and AI capabilities in real\-world customer and enterprise environments. This role works at the intersection of engineering, security operations, data, and customer success to translate complex requirements into scalable, production\-ready solutions.
This individual partners closely with security leaders, analysts, platform teams, data scientists, and infrastructure stakeholders to implement AI\-enabled SOC workflows, integrate security tooling, improve analyst experience, and accelerate time to value. The ideal candidate combines strong hands\-on engineering skills with a deep understanding of SOC operations, cloud platforms, automation, and customer\-facing delivery.
### Responsibilities
Customer and Stakeholder Delivery
- Serve as the technical lead for deploying and operationalizing security and AI solutions in customer or enterprise environments
- Translate business, operational, and security requirements into deployable architectures and implementation plans
- Partner with internal and external stakeholders to ensure solutions are aligned to operational goals, compliance requirements, and long\-term platform strategy
- Act as a trusted advisor during onboarding, implementation, rollout, and optimization phases
Solution Implementation and Integration
- Design and implement integrations across SIEM, XDR, SOAR, case management, data platforms, and AI\-enabled tooling
- Build and configure cloud\-native data ingestion, normalization, and enrichment pipelines for security telemetry
- Integrate APIs, webhooks, message queues, and automation workflows across identity, endpoint, cloud, network, and application ecosystems
- Develop reusable deployment patterns, templates, and technical assets to accelerate future implementations
AI\-Enabled Security Operations
- Operationalize AI and automation use cases within security workflows, including alert enrichment, triage support, summarization, clustering, playbook selection, and analyst copilots
- Work with detection engineering and data teams to support implementation of behavioral analytics, anomaly detection, risk scoring, and other AI\-assisted security use cases
- Help define data requirements, feedback loops, and operational guardrails needed to support effective AI outcomes in production environments
- Ensure deployed solutions are practical, measurable, and aligned to analyst workflows and response objectives
Automation and Reliability
- Implement secure and governed automation for investigation and response use cases across heterogeneous environments
- Support resiliency, observability, performance, and scale requirements for deployed solutions
- Troubleshoot integration issues, deployment blockers, and production challenges in partnership with platform, cloud, and security teams
- Improve reliability and maintainability through documentation, testing, monitoring, and standardized engineering practices
Cross\-Functional Leadership
- Collaborate across Security Operations, Security Engineering, Detection Engineering, Data Science, Infrastructure, and product or customer teams
- Communicate technical concepts clearly to both technical and non\-technical stakeholders
- Mentor engineers and contribute to best practices for implementation, delivery, and technical solution design
- Provide field feedback to influence platform roadmap, product direction, and architectural standards
### Required Qualifications
- 5\+ years of experience in security engineering, platform engineering, forward deployed engineering, solutions engineering, or related technical roles
- Hands\-on experience implementing and operating modern SOC technologies such as SIEM, XDR, and SOAR
- Experience deploying cloud\-native architectures on at least one major cloud provider such as AWS, Azure, or GCP
- Strong background in data integration, telemetry pipelines, normalization, and security analytics workflows
- Experience working directly with customers, internal stakeholders, or cross\-functional delivery teams in implementation\-focused environments
- Ability to lead technical engagements, drive execution, and influence outcomes without direct authority
### Technical Skills
- Strong understanding of security operations, detection and response, cloud security, identity security, and endpoint security
- Experience with scripting and automation using tools such as Python, PowerShell, or Bash
- Familiarity with APIs and integration patterns including REST, webhooks, and event\-driven workflows
- Working knowledge of AI and ML concepts relevant to SOC use cases, including anomaly detection, behavior analytics, LLMs, vector search, and AI assistants
- Experience with security data and analytics platforms such as Kafka, Kinesis, Pub/Sub, Spark, Databricks, BigQuery, Snowflake, or similar technologies is a plus
- Strong problem\-solving skills, execution mindset, and ability to operate effectively in ambiguous, fast\-moving environments
### Preferred Qualifications
- Experience in MSSP, MDR, XDR, or other security service delivery environments
- Experience deploying solutions in regulated or compliance\-sensitive environments
- Familiarity with infrastructure as code, CI/CD workflows, and production software delivery practices
- Experience building reusable implementation frameworks or field engineering playbooks
- Relevant certifications are a plus, including cloud, security, or platform\-specific certifications
### Education
- Bachelor’s degree in Computer Science, Information Security, Engineering, or equivalent practical experience
*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 privacy@ahead.com.
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-$200K range is above 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $160K to $200K.
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.
Ahead AI Hiring
Ahead has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $300K.
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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