Interested in this AI/ML Engineer role at Innovation Associates, Inc.?
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The Role:
iA is looking for a Sr. Director, Architecture and AI to lead enterprise\-wide architectural transformation across the organization. This is a hands\-on leadership role for someone who has modernized legacy monolithic systems, brings an AI\-first mindset, and can translate technical strategy into practical, scalable solutions in a fast\-paced growth environment.
In this role, you will set architectural direction while staying close to the work — partnering with engineers, reviewing designs, shaping governance, and contributing to reference implementations and prototypes that move the organization forward. You will help decompose iA’s long\-standing .NET/VB6 monolith into modern, scalable services while introducing AI\-native architecture, disciplined AI governance, and production\-ready AI capabilities across the company.
You will also provide leadership for iA’s cloud\-based Enterprise Analytics \& Insights platform and serve as a key connector between software, data, AI, and hardware teams. Success in this role will be measured by the business and customer outcomes modernization enables — including improved scalability, operational efficiency, product velocity, and long\-term growth.
What you’ll do:
Support Monolith Modernization \& Services Architecture
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- Lead the strategy and execution for modernizing the .NET/VB6 monolith into scalable, independently deployable services while balancing business continuity, technical risk, and delivery pace.
- Lead the definition of target\-state architecture, service boundaries, data ownership, and integration patterns, translating architectural strategy into working reference implementations and prototypes.
- Lead strategic trade\-off decisions across build vs. buy, rewrite vs. wrap, and speed vs. architectural rigor, clearly connecting recommendations to business impact.
AI\-First Architecture \& Scalable AI Practices
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- Set the architectural direction for AI\-first service design, embedding AI capabilities into core logic, workflows, and decision points rather than treating them as downstream enhancements.
- Establish a scalable AI practice for iA, including reusable patterns, shared infrastructure, and reference architectures that accelerate consistent, enterprise\-wide AI adoption.
- Serve as a member of the Core AI Governance team with responsibility for end\-to\-end governance of production AI systems, including model risk, data privacy, human\-in\-the\-loop controls, evaluation, monitoring, safety guardrails, and issue response.
Agentic Interfaces for Developer \& QA Productivity
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- Lead the architecture, development, and scaling of production\-grade AI agents, bringing hands\-on expertise in agent orchestration, tool use and permissioning, memory and context management, cost and latency optimization, and failure handling.
- Set technical standards for production AI agents, including orchestration, tool use and permissioning, memory and context management, cost and latency optimization, and failure handling at scale.
- Establish governance and operational practices for evaluating, monitoring, and safely rolling back agents in production, ensuring productivity gains do not compromise code quality, security, or reliability.
- Advance agentic tooling as an enterprise capability, creating reusable patterns that scale developer and QA productivity gains across teams.
NEXiA Platform Scaling — PoDs, SSP, MDS
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- Lead AI\-enabled scaling strategies for iA’s PoDs, SSP, and MDS within the NEXiA platform, improving throughput, reliability, and operational efficiency as platform usage grows.
- Ensure scaling strategies for PoDs, SSP, and MDS are informed by hardware dependencies, physical system constraints, and operational requirements across the NEXiA platform.
Customer Lifecycle Metrics — Land, Adopt, Expand, Retain
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- Use Land, Adopt, Expand, and Retention (LAER) metrics to guide architecture and AI investment decisions, ensuring technical priorities are directly tied to measurable customer adoption, expansion, retention, and business growth.
- Land: Accelerate onboarding and initial implementation through AI\-enabled workflows that shorten time\-to\-first\-value for new customers.
- Adopt: Increase feature and workflow adoption by using AI and agentic tooling to surface next\-best actions for customers and internal teams.
- Expand: Use AI\-informed signals to identify upsell and cross\-sell opportunities, ensuring platform architecture supports growth without significant rework.
- Retain: Apply AI\-driven early warning signals to identify churn risk, reliability concerns, or performance issues before they become customer\-facing problems.
- Translate architecture and AI initiatives into LAER\-based business narratives for executives and PE sponsors, showing how technical investments drive customer adoption, expansion, retention, and growth.
Hands\-On Technical \& People Leadership
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- Provide hands\-on technical leadership through production code contributions, pull request reviews, architecture spikes, and proof\-of\-concept development that validates architectural direction.
- Influence strategic decisions through technical credibility, data\-informed recommendations, and working prototypes that demonstrate feasibility and business impact.
- Lead and unify a 6–10\-person team of architects and engineers across multiple R\&D teams around shared architecture and AI standards, while balancing team\-specific priorities, constraints, and delivery needs.
- Build, mentor, and mature an architecture and AI engineering bench that can sustain and scale modernization, AI\-first architecture, and agentic tooling capabilities across the company.
Enterprise Analytics \& Insights Platform (Cloud)
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- Help and support iA’s cloud\-based Enterprise Analytics \& Insights platform, ensuring data quality, platform scalability, and AI/agentic capabilities are aligned across the teams that build and operate it.
- Align the platform’s architecture with the broader modernization strategy to ensure consistent technical direction across the enterprise.
- Use the platform to define, validate, and scale reusable AI\-first patterns, including insight generation, anomaly detection, and agentic analytics, that can be adopted across the enterprise.
Partnership with Hardware Teams
- Lead architectural alignment between software, AI, and hardware teams, ensuring system designs account for hardware dependencies, integration points, and physical constraints.
- Identify and lead AI and agentic opportunities within hardware\-adjacent workflows, including operational monitoring and scaling decisions for PoDs, SSP, and MDS across the NEXiA platform.
- Lead cross\-functional alignment between software and hardware teams, ensuring architecture decisions reflect full\-system trade\-offs across software, hardware, integration, and operational constraints.
Who you are:
- 15\+ years in software architecture and engineering leadership, including direct, hands\-on ownership of large\-scale system modernization.
- Demonstrated success leading the modernization of legacy monolithic applications, including .NET, VB6, or comparable legacy stacks, into scalable, modern services architectures.
- Deep, working knowledge of LLMs and agentic AI systems, including how they are architected, deployed, evaluated, secured, and scaled in production environments.
- Leadership of production AI agent initiatives, including agentic developer and QA tooling such as code generation, automated test generation, code review, or migration agents.
- Background owning or architecting cloud\-based enterprise analytics, insights, or data platforms at company\-wide scale
- Experience applying Land, Adopt, Expand, and Retention (LAER) or comparable customer\-lifecycle metrics to connect data, AI, and product strategy with customer adoption, expansion, retention, and business growth.
- Proven ability to lead, develop, and align mid\-sized engineering and architecture teams across multiple product or functional areas.
- Proven ability to lead across software and hardware/physical systems, including shared architecture decisions and technical trade\-offs.
- Experience in a growth\-equity environment where technical decisions are explicitly tied to value creation and exit outcomes.
- Track record of making complex technical and business trade\-offs and communicating decisions clearly to engineering and executive audiences.
Even better if you have:
- Experience with platforms similar in nature to NEXiA — high\-throughput operational systems with distributed processing units and service scaling demands.
- Background bridging enterprise IT and R\&D, having driven transformation across an organization rather than within a single function.
- Familiarity with regulatory or compliance\-heavy environments where AI safety and governance carry real operational stakes.
- Direct experience building internal developer\-productivity or QA platforms and measuring their adoption and impact.
Applicants must be authorized to work for ANY employer in the U.S. Employer will not sponsor applicants for work visas.
Compensation:
The estimated base annual salary range for this position is $212,500\.00 to $287,500\.00, though a candidate’s base annual salary shall be determined on a range of factors, including, but not limited to, qualifications and experience. This position may additionally be eligible for an annual discretionary bonus.
What are the perks?
- Generous time off policy that allows you to put your family first
- Opportunity to work on the cutting edge of pharmacy automation in a high growth tech company
- Competitive benefits, salary, and talent development opportunities
- Commitment to professional development and working for a company where your voice is heard
More about iA:
iA® (Innovation Associates®) is a pharmacy fulfillment company that provides an integrated platform of capabilities to support Centralized and Community Pharmacy Fulfillment Solutions. With over 30 years of experience in the pharmacy fulfillment business, we have developed and implemented a suite of automation and software solutions that help deliver quick and sustainable business results. Our integrated Pharmacy Fulfillment Platform enables scalable solutions that helps run the prescription fulfillment process from prescription acceptance to delivery, supporting dynamic design flexibility to service pharmacies in a variety of volumes and settings. Our solutions improve workflow, and increase efficiency, while enabling more time for pharmacists to focus on their patients. iA works with pharmacy providers in the Commercial, Health Systems, Government, and Mail Order/eCommerce markets. iA can help customers transform their pharmacy. For more information, visit iARx.com.
Our Mission: We partner with providers to transform pharmacy through our leading\-edge software enabled fulfillment technology and partners to deepen the patient\-pharmacist relationship, enhancing patient safety and choice while increasing operational efficiency. iA empowers pharmacists to focus on patient care. iA can run the prescription fulfillment process from start to finish, helping pharmacies manage fulfillment and inventory to help lower costs, improve efficiency, increase safety, and provide comprehensive Rx tracking and real\-time support.
Our Products:
- Software
- Modular Hardware
- Sophisticated Counting and Collation Devices
Our Core Values:
- Solutions Driven
- Customer Centric
- Championing Diversity
- Empowering Ownership
- Trust Daringly
To learn more about iA’s product, people and culture visit us at iARx.com OR check us out on LinkedIn, Facebook, or YouTube!
*iA provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
*This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.*
\#LI\-DNI
Salary Context
This $212K-$287K 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 Innovation Associates, Inc., 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($250K) sits 16% above the category median. Disclosed range: $212K to $287K.
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.
Innovation Associates, Inc. AI Hiring
Innovation Associates, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $287K - $287K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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