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About This Role
Job Description About Persistent
We are an AI-led, platform-driven Digital Engineering and Enterprise Modernization partner, combining deep technical expertise and industry experience to help our clients anticipate what's next. Our offerings and proven solutions create a unique competitive advantage for our clients by giving them the power to see beyond and rise above. We work with many industry-leading organizations across the world, including 20 Fortune 50 companies and 4 of the 5 top banks in both the US and India, and numerous innovators across the healthcare ecosystem.
Our disruptor's mindset, commitment to client success, and agility to thrive in the dynamic environment have enabled us to sustain our growth momentum. Persistent has been recognized across top industry platforms for innovation, leadership, and inclusion. We reported $1,654.4M FY26 revenue with 17.4% Y-o-Y growth. We have delivered 24 sequential quarters of growth with $436.0M in Q4 FY26 revenue, up 3.2% Q-o-Q and 16.2% Y-o-Y growth. Our 27,500+ global team members, located in 18 countries, have been instrumental in helping the market leaders transform their industries.
About Position:
We are seeking an experienced Sr AI Engineering Lead to drive the strategy, architecture, and delivery of enterprise-scale AI, Data, and Analytics solutions. The ideal candidate will possess deep expertise in Generative AI, cloud-native application development, and enterprise architecture, along with strong leadership capabilities. This role will be responsible for designing innovative AI-powered solutions using Azure OpenAI and modern AI frameworks while leading engineering teams to deliver scalable, secure, and business-focused digital transformation initiatives.
- Role: Sr AI Engineering Lead
- Location: Bellevue, WA
- Experience: Between 12 to 15 Years
- Job Type: Full Time Employment
What You'll Do:
- Lead the architecture, design, and implementation of enterprise AI solutions utilizing Azure OpenAI, Retrieval-Augmented Generation (RAG), Semantic Kernel, Embeddings, Prompt Engineering, and Agentic AI frameworks.
- Drive end-to-end delivery of enterprise AI transformation programs, including AI Copilots, AI Agents, and intelligent automation solutions.
- Design and develop cloud-native applications using .NET/C#, RESTful APIs, microservices, and Azure-first architecture patterns.
- Collaborate with business leaders, product owners, architects, and engineering teams to define technology roadmaps and implementation strategies.
- Translate complex business requirements into scalable, secure, and sustainable enterprise solutions.
- Architect and implement enterprise-grade Generative AI solutions that improve business processes and user experiences.
- Lead AI strategy discussions and recommend innovative technologies to support organizational objectives.
- Oversee Azure DevOps pipelines, CI/CD processes, deployment automation, release management, and application lifecycle management.
- Drive best practices for software engineering, AI engineering, architecture governance, and cloud adoption.
- Conduct architecture reviews, technical design sessions, solution assessments, and code reviews.
- Ensure solution reliability, scalability, performance optimization, security, and compliance across platforms.
- Establish AI governance frameworks, Responsible AI practices, and enterprise security standards.
- Mentor technical teams and foster a culture of innovation, collaboration, and engineering excellence.
- Support production operations, incident management, root cause analysis, and continuous improvement initiatives.
- Partner with executive leadership and stakeholders to drive measurable business outcomes through AI programs.
Expertise You'll Bring:
- 12 to 15 years of experience in software engineering, cloud architecture, AI engineering, and enterprise solution delivery.
- Strong hands-on experience with Azure OpenAI, Generative AI, Large Language Models (LLMs), RAG architectures, Semantic Kernel, Prompt Engineering, Embeddings, and Agentic AI frameworks.
- Proven expertise in designing and delivering enterprise-scale AI transformation initiatives and AI-powered business solutions.
- Extensive experience developing cloud-native applications using .NET/C#, REST APIs, and microservices architectures.
- Strong understanding of Microsoft Azure services and modern cloud architecture patterns.
- Experience delivering enterprise Copilot implementations and AI Agent-based solutions.
- Strong expertise in Azure DevOps, CI/CD pipelines, deployment automation, release management, and production support.
- Experience creating scalable architecture roadmaps and technology transformation strategies.
- Strong understanding of enterprise integration, API design, security controls, and cloud-native development practices.
- Excellent stakeholder management skills with the ability to communicate effectively with business executives and technical teams.
- Experience leading architecture governance, engineering standards, solution reviews, and technical mentorship programs.
- Strong knowledge of performance optimization, reliability engineering, scalability, data governance, and operational excellence.
- Understanding of Responsible AI principles, AI governance frameworks, data privacy regulations, compliance requirements, and enterprise security standards.
- Experience with Azure Bicep, Infrastructure as Code (IaC), PowerShell, and cloud automation technologies.
- Proven leadership experience managing high-performing engineering teams and cross-functional initiatives.
- Strong analytical, problem-solving, and strategic planning skills.
- Microsoft Azure certifications in AI, Solution Architecture, or related cloud technologies are highly preferred.
- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related discipline.
Benefits:
- Competitive salary and benefits package
- Culture focused on talent development with quarterly growth opportunities and company-sponsored higher education and certifications
- Opportunity to work with cutting-edge technologies
- Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards
- Annual health check-ups
- Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents
Values-Driven, People-Centric & Inclusive Work Environment:
Persistent is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds.
- We support hybrid work and flexible hours to fit diverse lifestyles.
- Our office is accessibility-friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities.
- If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment.
Let's unleash your full potential at Persistent - persistent.com/careers
"Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind."
1 Open Positions .Net Core,Rest API,Microservices Skills Required Bellevue, Washington, United States (USA) Location .Net Core,Rest API,Microservices Desirable Skills 186614 Job Code
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Persistent Systems, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Persistent Systems AI Hiring
Persistent Systems has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New Jersey, United States, Dallas, TX, Bellevue, WA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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