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About This Role
Who we are
DigiCert is a global leader in intelligent trust. We protect the digital world by ensuring the security, privacy, and authenticity of every interaction. Our AI-powered DigiCert ONE platform unifies PKI, DNS, and certificate lifecycle management, to secure infrastructure, software, devices, messages, AI content and agents. Learn why more than 100,000 organizations, including 90% of the Fortune 500, choose DigiCert to stop today's threats and prepare for a quantum-safe future at www.digicert.com
Job summary
You will embed with various business functions (Sales, Support, Finance, Legal, HR, Procurement, Marketing), learn how the work actually gets done, and build the AI solutions that change it: automated workflows, agents on our systems of record, and AI-native tools wired into the platforms people already use. You work in a pod with a Senior Business Architect (who owns the problem framing and business case) and an operator from the function (who owns adoption). You own the solution: design, build, launch, measure, iterate.
This is not a research role and it is not a traditional software engineering seat. It is the person who can sit in a Finance close review on Monday, prototype an agent against NetSuite and Salesforce data by Wednesday, and walk the CISO through the access model on Friday. You will be measured on production value the business can point to, not pilots.
What you will do
Embedded problem solving and delivery. Sit inside a function for the length of an engagement. Map the workflow, find where AI removes friction or creates capacity, and ship the fix. Own each solution end to end, from discovery through production monitoring. Three to four production launches a year is the bar.
Automation, agents, and AI-native tools. Build on our orchestration and platform layer (Salesforce and Agentforce, Microsoft 365 and Copilot, Slack, AWS and frontier model APIs, and the integration tooling that connects them). Design agent workflows with retrieval, tool use, evaluation, and human-in-the-loop review built in. Configure and extend vendor AI tools before building custom. Know when each is the right call.
AI infrastructure and vendor architecture. Partner with IT, Security, and Engineering to design the shared foundation every production agent needs: model access, identity and permissions, data connectivity, logging and observability, evaluation. Lead the technical side of vendor evaluations and proofs of concept. Turn one-off wins into reusable patterns the next pod can pick up.
Enablement for everyday users. Design the services that help regular employees use AI well: templates, guided workflows, office hours, a path from "I have an idea" to "it runs in production." Coach power users so workflows outlive the person who built them. Success is the business owning and extending what you built.
Intake and governance, the technical half. Serve as the technical reviewer in DigiCert's AI intake and governance process. Assess proposed use cases for data exposure, security posture, and feasibility. Recommend the right build path (vendor tool, configuration, custom). Keep every AI deployment safe, auditable, and inside DigiCert's security and compliance requirements, including FedRAMP-adjacent constraints where they apply.
ROI and measurement. Instrument what you build. Every solution ships with a baseline, a metric, and a dashboard the sponsoring executive can read. Feed results into the company AI measurement program run with the CFO.
First 90 days
- Days 1 to 30: learn the stack and the governance process. Shadow the current pod. Review in-flight intake requests.
- Days 31 to 60: ship a first production workflow or agent with the pod.
- Days 61 to 90: second engagement in flight. First enablement service live (template library, office hours, or guided intake). Present measured results from the first launch to the sponsoring executive.
What great looks like
- Business leaders ask for you by name because the last thing you built is still running and still measured.
- You can explain a permissions model to the CISO and a workflow change to a sales rep in the same afternoon.
- You default to the smallest solution that works, and you know when a vendor tool beats a custom build.
- You leave behind patterns, documentation, and trained operators, not dependency on you.
- You treat governance as a design input.
What you will have
- 5+ years building and shipping technical solutions in a business context: solutions engineering, business systems, integration, automation, or forward-deployed work.
- 2+ years hands-on with LLM-based systems in production or near-production: agent frameworks, retrieval, tool calling, prompt and evaluation design.
- Working proficiency in Python or JavaScript and comfort with APIs, webhooks, data pipelines, and authentication (OAuth, SSO, role-based access). You do not need to be a career software engineer, but you need to build and debug without waiting on one.
- Experience with enterprise platforms: Salesforce, Microsoft 365, NetSuite, Jira, and modern data warehouse.s
- A track record of working directly with non-technical teams and translating messy processes into working systems.
- Judgment in security- and compliance-sensitive environments. You know what data cannot leave the building and design accordingly.
- Clear written and spoken communication. You can write a one-page architecture memo an executive will actually read.
Nice to have
- Prior forward-deployed, solutions architect, or embedded consulting experience.
- Hands-on with Agentforce, Copilot Studio, AWS Bedrock, Anthropic or OpenAI APIs, or MCP-based tool integration.
- Experience with intelligent automation or workflow platforms (Power Automate, Workato, n8n, UiPath).
- Exposure to SOX, FedRAMP, or similar control environments.
- Experience in a security or PKI company, or another business where trust is the product.
Benefits
- Competitive compensation and comprehensive health, dental, and vision coverage
- Retirement savings programs with company matching (401(k) or RRSP)
- Generous paid time off, including holidays, and vacation
- Paid parental leave and family support benefits
- Life and disability coverage
- Flexible spending and health savings options (where applicable)
- Health and wellness support, including gym reimbursement and wellness programs
- Employee Assistance Program with 24/7confidential support for employees and families
- Education assistance and professional development opportunities
- Access to LinkedIn Learning and continuous learning resources
- Employee referral bonus program and additional company perks and discounts
- Internal rewards and recognition platform (Motivosity) to celebrate and acknowledge project wins, milestone achievements, and the outstanding contributions of our colleagues
- Business travel insurance and global employee support programs
To protect candidate information and maintain a secure hiring process, all applications must be submitted through our careers portal. Resumes or CVs sent directly via email will not be reviewed or considered.
DigiCert is an Equal Opportunity employer and is committed to diversity in its workforce. In compliance with applicable federal and state laws, DigiCert prohibits discrimination on the basis of race or ethnicity, religion, color, national origin, sex, age, sexual orientation, gender identity/expression, veteran's status, status as a qualified person with a disability, or genetic information. Individuals from historically underrepresented groups, such as minorities, women, qualified person with disabilities, and protected veterans are strongly encouraged to apply.
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Salary Context
This $170K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At DigiCert, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $170K to $220K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
DigiCert AI Hiring
DigiCert has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span OR, US, Sunnyvale, CA, US, Lehi, UT. Compensation range: $220K - $240K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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