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About This Role
About Patra:
Patra is an outcome\-focused operating partner working exclusively for the insurance industry. Combining expert global teams, process intelligence, and purpose\-built technology, Patra delivers measurable results to clients. With 20 years of deep domain expertise, exceptional client retention rates, and significant sustained investment in AI, Patra partners with agencies, brokers, MGAs, MGUs, and carriers to transform how insurance operations are delivered.
Core Duties:
The Technical Project Manager will drive the AI Development Lifecycle (AI‑DLC) where AI agents contribute across development and quality processes, and Human‑in‑the‑Loop (HITL) engineers govern their output. Acting as delivery lead, change agent, and AI practitioner, this role adapts delivery rituals, sets governance and review standards, and embeds AI tooling into daily workflows to keep the human layer effective and accountable.
Working with Product and Engineering counterparts, the Technical Project Manager will act as the Project Management leader to create, implement and improve delivery models and drive development outcomes. SummaryResponsible
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Lead adoption of the AI Development Lifecycle and HITL delivery, and develop governance, policy, and tooling to ensure scalable, auditable human oversight of agent‑augmented work. Keep current with rapid advancements in software development to drive continuous improvements in the AI\-DLC. Accountable
In partnership with Product and Engineering, drive development delivery outcomes, compliance, auditability, and the integrity of human oversight and governance for agent‑contributed work. Consultative Management and Oversight
Coach HITL leads and stakeholders, develop policy and governance artifacts, align cross‑functional engagement models, and translate delivery metrics into actionable guidance to scale repeatable, auditable hybrid human‑agent delivery. Quantitative Measurements* Success Criteria Management
- CSAT for Product and Engineering
- Audit pass rate/compliance score
- Time to Restore Service (MTTR)
- Lead Time from Idea to Delivery
- Token cost per deliverable
- Agent autonomy
- Code quality
Core Management Responsibilities – Across All Service Lines* Strategic direction — drive roadmap execution for AI‑DLC and HITL adoption across teams and client engagements.
- Outcomes accountability — ensure and track client outcomes from engineering projects. Projects are primarily agentic and require AI fluency to manage.
- Governance \& policy — contribute and enforce HITL governance, review standards, audit cadence, and compliance requirements.
- People leadership \& coaching — mentor engineers, product owners, and cross‑functional stakeholders on hybrid delivery practices and responsible AI use.
- Stakeholder alignment — coordinate with Sales, Client Success, Security, and Finance to align delivery with commercial and risk objectives.
- Performance measurement — set KPI framework (DORA/SPACE adapted for HITL), establish targets, and translate metrics into executive narratives.
- Vendor \& partner oversight — set briefs, success metrics, and quality standards for external partners and managed services.
Core Operational Responsibilities* Program delivery management — plan, prioritize, and oversee end‑to‑end delivery across teams, managing scope, schedule, risks, and dependencies. Data driven reporting is an essential foundation.
- Tooling ownership — administer Jira and Confluence configurations, automation rules, templates, and data hygiene to reflect HITL workflows.
- Quality assurance \& incident response — monitor change failure rate, MTTR, and remediation actions; ensure human oversight on agent‑contributed work.
- Automation \& scale — design and implement automation to reduce manual review where safe, and scale repeatable delivery motions.
- Reporting \& dashboards — produce weekly/monthly dashboards (deployments, lead time, human review pass rate, audit status) for teams and leadership.
- Audit \& compliance execution — run audits, document findings, and implement remediation to maintain traceability and regulatory readiness.
Key Performance Indicators* Deployment Frequency — number of production deployments per period.
- Lead Time for Changes — median time from commit to production.
- Change Failure Rate — % of deployments causing incidents or rollbacks.
- Time to Restore Service (MTTR) — median time to recover from incidents.
- Team Satisfaction — Team satisfaction for Product and Development.
- Human Review Pass Rate — % of agent‑contributed outputs accepted without rework after human review.
- Automation Coverage — % of validation/review steps automated vs manual.
- Agent Autonomy \- % of agent vs human time.
- Token Cost controls – staying within allocated token budgets.
- Audit / Compliance Pass Rate — % of audits passed or compliance checklist score.
- Jira/Confluence Data Hygiene — % of tickets with required fields; % of stale tickets closed.
- Operational Efficiency — reduction in manual review cycles; % improvement in lead time or MTTR over baseline.
Qualifications \& Experience (Required):* 8\+ years of project or program management experience within a software development organization including experience in distributed engineering, data science, and operations.
- First\-hand experience setting up and managing agentic development processes in a commercial setting with Claude Code or Codex, with the resulting code working in production.
- Demonstrated project experience with LLM/agentic systems delivered to production
- Proven experience coaching and leading teams through process or structural transitions.
- Daily practical fluency with LLM tools (Claude, Gemini, ChatGPT, or equivalent) and ability to teach others.
- Hands‑on Jira administration: workflows, automation, dashboards, advanced roadmaps.
- Confluence administration: space architecture, templates, permissions, and content governance.
- Working knowledge of DORA metrics and the SPACE framework and experience operationalizing outcomes‑based KPIs.
- Comfort with HITL models and environments where AI agents contribute under human oversight.
- Strong facilitation, communication, and stakeholder management skills.
- Demonstrated audit experience with regulatory bodies (ISO or equivalent)
Preferred (Nice\-to\-Haves):* Familiarity with engineering intelligence platforms (Jellyfish, LinearB, Waydev, Faros.ai)
- Experience in regulated industries (insurance, financial services)
- Hands‑on experience with GitHub/GitLab or complementary developer tooling
- Background working with managed services or external delivery partners
- Practical experience in prompt engineering and LLM workflow design
Equal Employment Opportunity:
Patra Corporation is an equal opportunity employer committed to celebrating diversity and creating a safe and inclusive environment for all employees.
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 Patra Corporation, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Patra Corporation AI Hiring
Patra Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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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