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Overview:
We are seeking a Senior AI Engineer to define and drive the end\-to\-end engineering of an enterprise\-grade agentic orchestration capability that enables smart AI agents to autonomously execute workflows, collaborate with humans, and operate securely with governed access. This role owns the technical direction and delivery of core capabilities spanning agent workflow development environments, automated CI/CD and safe migration patterns, human–agent collaboration and long\-running orchestration, and agent identity/registry/marketplace with policy enforcement. You will serve as the technical authority—establishing standards for reliability, auditability, security, and performance; driving cross\-team execution; and ensuring adoption at scale through enablement and strong operational practices.
Responsibilities:
1\) Technical Direction, Architecture Standards \& Roadmap Ownership (30%)* Define reference architecture, design standards, and engineering guardrails for agent workflow orchestration, human collaboration, and identity/governance capabilities. *(Decide/Consult)*
- Own sequencing of releases, deprecation strategy, and compatibility standards to enable safe evolution with minimal disruption. *(Decide)*
2\) Secure\-by\-Design Identity, Policy Enforcement \& Auditability (25%)* Establish and enforce non\-human identity patterns, consent propagation mechanisms, RBAC/ABAC policy models, and least\-privilege access across agent workflows. *(Decide/Consult)*
- Ensure end\-to\-end auditability for agent actions, prompt/tool changes, model switches, handoffs/messages, approvals, and access decisions; define evidence requirements for compliance. *(Decide/Consult)*
- Define and enforce data classification, PII redaction, retention/purge, and policy\-based routing to compliant models/providers. *(Decide/Consult)*
3\) Deterministic Human–Agent Collaboration \& Long\-Running Orchestration (20%)* Define and drive implementation of deterministic handoff patterns (assign/escalate/co\-pilot/co\-author), resilient messaging, and stateful long\-running workflows with timers and compensation/rollback. *(Decide/Consult)*
- Ensure seamless integration into enterprise systems (CRM/ITSM/custom apps) via gateways and standardized interfaces. *(Consult/Decide)*
4\) Automated Delivery, CI/CD Gates \& Safe Migration Patterns (15%)* Define promotion gates and automated CI/CD standards including versioning, testing, security scans, approvals, and drift detection. *(Decide/Consult)*
- Drive safe migration practices between model providers/versions with minimal downtime and proven rollback; define operational playbooks. *(Decide/Consult)*
5\) Operational Excellence, Reliability \& Enablement (10%)* Own SLIs/SLOs and operational posture: observability standards (metrics/logs/traces), incident and credential compromise runbooks, and release readiness reviews. *(Decide/Consult)*
- Deliver enablement: reference implementations, developer playbooks, training for platform ops and application teams; mentor senior and junior engineers. *(Consult/Execute)*
Decision\-Making Autonomy: High — accountable for architecture standards, cross\-team technical tradeoffs, governance posture, and operational readiness decisions.
Supervision Required: Low — operates with periodic alignment to senior leadership and governance forums.
Complexity of Role: Very high — enterprise\-grade orchestration with strict security/audit requirements, multi\-tenant isolation, deterministic workflow needs, and latency SLOs across multiple integrated systems.
Cross\-Functional Interactions: Yes — leadership\-level engagement across security/identity, DevX, SRE, enterprise applications, and business/product stakeholders.
Qualifications:
Key Skills/Experience Required Minimum Qualifications: Minimum Qualifications* Bachelor’s in CS/AI/ML/Data Science or equivalent experience required.
- Master’s preferred
- 10 year experience in ML, Data Science, AI required.
- Extensive experience designing and operating enterprise platforms/services with production reliability and governance requirements.
Required Expertise* Systems/platform architecture: multi\-tenant isolation, scalability, versioning, backward compatibility, release sequencing
- Orchestration and workflow systems: Temporal\-class systems (or equivalent) including long\-running workflows, compensation, state persistence
- Identity and security architecture: SSO (SAML/OIDC), non\-human identity, RBAC/ABAC, consent propagation, secrets/keys rotation, least\-privilege design
- Governance and compliance engineering: audit logging models, approval workflows, policy routing, PII redaction, retention/purge controls
- Observability/SRE partnership: SLO definition, OTel\-based telemetry, incident management, reliability engineering
- Developer enablement: SDK design, reference implementations, platform adoption strategy, mentoring and technical leadership
Differentiating Competencies* Strategic thinking: shapes direction and standards; anticipates second\-order impacts of platform decisions
- Proactiveness \& initiative: identifies systemic risks early (security, reliability, adoption) and drives resolution
- Discretion: handles sensitive security/identity, compliance, and access\-control topics appropriately
- Financial acumen: frames tradeoffs across build vs buy, provider choices, operational cost and risk
- Executive communication: crisp narratives for governance forums; evidence\-based recommendations and decisions
- Organizational leadership: aligns multiple teams, mentors senior engineers, drives adoption and accountability
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 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At PepsiCo, 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 $175,000 based on 11,128 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,500.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
PepsiCo AI Hiring
PepsiCo has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US. Compensation range: $156K - $185K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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