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About This Role
Job Description: Remote role with potential travel to Indianapolis office or hybrid if residing in Indiana
Base salary range: $130\-150k with 12% annual bonus
This role is designed for a senior technology leader who can shape and drive the architecture vision for Generative AI across the Life \& Annuities business. The Technical Architect will play a critical role in transforming insurance operations by identifying automation opportunities, defining scalable AI solution patterns, and ensuring seamless integration across TPA platforms, policy administration systems, underwriting capabilities, and external partner ecosystems. This position requires both strategic thinking and hands\-on architectural leadership to deliver secure, compliant, and business\-focused AI innovation.
Role Overview
We are seeking an experienced Technical Architect to lead Generative AI initiatives within the Life \& Annuities insurance domain. This role will be responsible for architecting and guiding the implementation of AI\-driven solutions that automate and modernize business processes across the TPA ecosystem, including policy administration systems, underwriting platforms, and external integrations. The ideal candidate will combine deep technical architecture expertise with strong domain knowledge in insurance operations, enterprise integration, data governance, and responsible AI adoption.
- Responsibilities: Lead the end\-to\-end architecture, design, and technical direction for Generative AI solutions supporting business process automation in the Life \& Annuities ecosystem.
- Define scalable, secure, and reusable architecture patterns for AI\-enabled workflows across policy administration, underwriting, servicing, document processing, and partner integrations.
- Partner with business, operations, product, and engineering teams to identify high\-value AI use cases and translate them into implementable technical solutions.
- Architect enterprise\-grade AI solutions using modern patterns such as Retrieval\-Augmented Generation (RAG), prompt orchestration, agent\-based workflows, vector search, and human\-in\-the\-loop controls.
- Design and oversee integrations between AI services and core insurance platforms, TPA systems, underwriting tools, CRM platforms, document repositories, APIs, messaging systems, and third\-party data providers.
- Establish solution standards for security, privacy, auditability, explainability, observability, and compliance in regulated insurance environments.
- Evaluate and recommend appropriate cloud, data, and AI technology stacks, frameworks, and tooling aligned with enterprise architecture standards.
- Provide technical leadership to cross\-functional delivery teams, including conducting architecture reviews, defining guardrails, mentoring engineers, and resolving complex design challenges.
- Drive non\-functional architecture requirements including performance, scalability, resiliency, model monitoring, and operational support readiness.
- Collaborate with enterprise architecture, security, data, and governance teams to ensure AI solutions are aligned with organizational policies and long\-term technology strategy.
- Qualifications: Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
- 10\+ years of experience in software engineering, solution architecture, or enterprise architecture, with strong hands\-on experience designing complex distributed systems.
- 3\+ years of experience delivering AI, machine learning, or Generative AI solutions in enterprise environments.
- Strong expertise in Life \& Annuities insurance processes, especially within TPA ecosystems, policy administration, underwriting, servicing, and external partner integrations.
- Demonstrated experience defining architecture for cloud\-native applications, APIs, event\-driven integration, and enterprise data platforms.
- Strong understanding of LLM\-based solution patterns, including prompt engineering, RAG, embeddings, vector databases, model orchestration, and AI application security.
- Experience working with cloud platforms such as AWS, Azure, or Google Cloud, along with associated AI and integration services.
- Strong knowledge of system integration patterns, data privacy, access control, and governance requirements in regulated environments.
- Ability to communicate complex technical concepts clearly to business stakeholders, project teams, and senior leadership.
- Proven ability to lead architecture decisions, influence technical direction, and collaborate across multiple teams and vendors.
Preferred Qualifications
- Experience delivering AI\-led automation in insurance operations such as intake, underwriting support, correspondence generation, document summarization, and workflow acceleration.
- Familiarity with commercial insurance platforms, policy administration systems, workflow tools, and enterprise integration middleware relevant to Life \& Annuities operations.
- Hands\-on experience with AI governance frameworks, model risk controls, content safety, and responsible AI practices.
- Experience evaluating and integrating vendor AI platforms, accelerators, and third\-party services.
- Architecture or cloud certifications such as AWS Solutions Architect, Azure Solutions Architect, or equivalent credentials.
- Experience in consulting, client\-facing architecture, or leading enterprise transformation programs is a plus.
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EXL Service, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($140K) sits 23% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EXL Service AI Hiring
EXL Service has 9 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Architect. Positions span US, Jersey City, NJ, US. Compensation range: $150K - $280K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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