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We are seeking a Sr. Principal, Product Manager to own the agentic and platform capabilities of the Conduct platform. This is a senior individual\-contributor role. You will set product direction and do the work directly. You will not manage a team. You will lead through technical depth, a high craft bar, and influence across product and engineering.
The mandate has two connected halves. First, define the API, MCP, and headless surfaces that let sophisticated customers and AI agents build on the platform rather than around it. Second, own the non\-functional requirements, scale, performance, reliability, multi\-tenancy, and security, that the largest regulated financial institutions require to move a deal through diligence. Both require a product leader who treats platform contracts and non\-functional requirements as product decisions, not engineering handoffs.
You will own the product strategy, roadmap, and execution for this surface, partnering directly with Engineering, AI/ML, Data Science, Professional Services, and the customer\-facing organizations to define what the platform is, who it serves, and how it wins.
### What you'll do?
Platform and Agentic Product Strategy
- Own the strategy and roadmap for Conduct’s agentic and platform capabilities: the API, MCP, and headless workflow surfaces that serve both human users and AI agents on a unified workflow layer and audit trail.
- Define the API surface as product: alert queues, review actions, reason\-code taxonomies, workflow configuration, and the programmatic contracts that agents call. These are product decisions, not engineering ones.
- Build the extensibility model: how sophisticated customers and external or agentic systems build on the platform, including developer\-facing documentation and the packaging that frames what is a subscription tier, what is usage\-based, and what justifies a premium price point.
- Partner with the AI/ML product team on the boundary between the intelligence layer and the workflow layer that agents call. Keep the workflow contract clean so agents call the Conduct workflow layer while the intelligence layer powers it from beneath.
Non\-Functional Requirements and Platform Readiness
- Own non\-functional requirements as first\-class product requirements: scale, performance, reliability, and the security posture that Tier 1 financial institutions require in diligence. Treat security, telemetry, and reliability as product surfaces.
- Translate the diligence and regulatory\-examination bar of the world's largest banks into a defensible non\-functional roadmap. In a regulated compliance product, a gap in any of these carries real risk.
Agentic Workflows
- Define product requirements for agentic workflows such as review, analyst assist, auto\-sampling, and automations, operating on the same behavioral data asset and audit trail as human users.
- Ensure every agent's action is captured, explainable, and defensible to the same standard as an analyst decision, so the audit trail holds up to regulatory examination.
Cross\-Functional Leadership
- Partner with Engineering on capacity allocation, release readiness, and operational discipline. You bring specifications complete enough for engineering to build without coming back for clarification.
- Partner with AI/ML, Data Science, Professional Services, and Customer Experience to translate customer pain into defensible priorities. Show up where customers are, not just where internal stakeholders are.
- Maintain a clear view of the competitive landscape and develop differentiated positioning grounded in product reality.
Technical Leadership and Influence
- Set the standard for platform and API/MCP product craft on the product team: problem framing, evidence\-based prioritization, and outcome measurement. You raise the bar through your own work and through influence, not through direct reports.
- Mentor product managers on platform and agentic thinking. This is a dotted\-line, lead\-through\-influence role, not a people\-management one.
### What you’ll bring
- 10\+ years of product management experience, with a track record of shipping product at enterprise scale.
- Demonstrated API and platform product ownership: you have defined and owned developer\-facing API surfaces or extensibility layers as product decisions and written the specs to prove it.
- Demonstrated non\-functional requirements ownership: you have shipped products where scale, performance, reliability, or security were product requirements you owned, not issues you delegated to engineering.
- Experience building for both human and programmatic or agentic consumers, or a strong adjacent track record. Familiarity with agent frameworks or MCP\-style surfaces is a plus.
- Experience with enterprise SaaS products in regulated industries. You understand what it means to ship software where wrong answers create legal or regulatory exposure.
- A track record of leading through technical authority and influence without formal reports. You have set direction and standards that others followed because of the quality of your work, not your title.
- Strong written communication. You write product specs that engineering can build from and strategy documents that executives can act on. These are different documents and you know the difference.
- Experience working in a cross\-functional model with Engineering, Design, and Data Science as genuine peers, not as service organizations.
### Preferred Qualifications
- Prior experience in financial services, compliance, legal technology, or another domain where AI explainability and auditability are not optional.
- Experience commercializing AI or ML capabilities: translating model performance into product value, and product value into pricing and packaging decisions.
- Experience managing products with both a UI surface and a developer\-facing surface simultaneously. The two require different decision frameworks, and you have operated in both.
- Experience with agent frameworks, MCP, or bring\-your\-own\-model style extensibility as a product concern.
### What do we offer?
- Healthcare insurance: We provide medical, dental, and vision insurance, and a flexible spending account that allows you to set aside pre\-tax dollars to pay for eligible out\-of\-pocket expenses.
- Stock options.
- Personal time off: A healthy work\-life balance is critical to your success at the office. Smarsh offers a “take\-what\-you\-need” time off policy as well as flexible work arrangements.
- 401K Match: Smarsh provides a 4% 401K match for which employees are fully vested on day one.
- Sabbatical: The Smarsh sabbatical programme provides a time to recharge, study or simply do something you are passionate about away from the workplace. Employees are eligible after six years of service.
- Recognition: We’re big on kudos for a job well done. Our employee\-recognition programme enables co\-workers to nominate their peers who best embody our core values for recognition.
The above salary range represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting.
Any applicable bonus programs will be discussed during the recruiting process.
The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training. Local cost of living assessments are done for each new hire at the time of offer.
Don't meet every requirement? Apply anyway! We value diverse candidates and encourage applications, even if you don't perfectly match the job description. Studies have shown that some strong candidates may self\-select out of the interview process prematurely, at Smarsh we encourage an inclusive, high\-performing environment.
Smarsh is an equal opportunity and affirmative action employer. Qualified applicants will receive consideration without regard to their race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Smarsh invites all qualified interested applicants to apply for career opportunities. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Including frequency of functions
Salary Context
This $220K-$240K range is above the 75th percentile 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 Smarsh, 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 in Demand for This Role
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 ($230K) sits 7% above the category median. Disclosed range: $220K to $240K.
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.
Smarsh AI Hiring
Smarsh has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, Remote, US. Compensation range: $110K - $240K.
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
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