Interested in this AI/ML Engineer role at Blue Cross Blue Shield of Massachusetts?
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About This Role
Ready to help us transform healthcare? Bring your true colors to blue.
This role has an in office expectation of 2X a week in our Boston location. We are in the office on Tuesdays and Wednesdays.
We are looking for an Associate Director, AI Solution Architecture to lead our Enterprise AI and Solution Architecture group. In this leadership role, you will translate enterprise AI strategy into coherent, governed, and production-viable solution architectures that span business lines, platforms, and geographies.
This role does not build the core platform or own feature delivery; rather, you will define how AI capabilities are assembled into complete, end-to-end business solutions. Operating as a critical technical peer to the AI engineering and delivery function, you will bridge the gap between complex business needs and scalable architecture to drive the adoption of intelligent AI assistants and agentic workflows across the enterprise. What You'll Do
Team Leadership & Mentorship: Lead, mentor, and manage a high-performing team of AI solution architects, providing technical guidance, architectural oversight, and career development.
Solution Design & Engineering Partnership: Partner with the AI Engineering team to conduct business needs assessments and identify high-impact agentic use cases. Translate complex business requirements into robust, scalable technical blueprints that enable production-grade engineering delivery.
Architect Agentic Solutions: Oversee the architectural design of AI assistants, establishing enterprise best practices for model selection, prompt orchestration, retrieval-augmented generation (RAG), and agent orchestration.
Multi-Agent Orchestration: Guide the design of intelligent, multi-step AI agents, defining patterns for sequential, loop, parallel, and hierarchical architectures to handle multi-turn, multi-domain use cases.
System Integration: Architect secure, scalable tool-use and function-calling patterns, ensuring AI agents connect seamlessly to enterprise APIs, databases, and third-party systems.
Responsible AI & Governance: Partner with AI Governance to structurally embed ethical considerations, fairness, transparency, hallucination mitigation, and strict data privacy into all AI solutions.
Production Scalability: Design highly available, production-grade solutions, collaborating closely with engineering leads to ensure architectural concepts translate into reliable, high-performance deployments.
Communication: Serve as the primary architectural liaison, effectively articulating the value, mechanics, and design of agentic AI solutions to non-technical business leaders and executive stakeholders. Required Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related technical field (or equivalent practical experience).
Experience: 8+ years in software engineering or solution architecture, with 3+ years specifically focused on designing large-scale AI/ML or LLM-based agentic solutions.
Leadership: 3+ years of experience directly managing and leading teams of technical architects or systems designers.
Cloud Expertise: Hands-on architectural experience with Google Cloud Platform (GCP) services, including Vertex AI Agent Builder, Vertex AI Search, BigQuery, Cloud Storage, and Cloud Run.
AI Architecture: Proven expertise in designing solution architectures leveraging Large Language Models (LLMs) , including RAG, embeddings, complex function calling, and multi-agent frameworks.
Integration & Systems: Strong command of API design , microservices, cloud-native development patterns, and integrating AI capabilities into existing enterprise product ecosystems.
MLOps Foundations: Solid knowledge of MLOps practices, including designing frameworks to measure and monitor LLM quality, latency, and cost in production.
Communication: Exceptional ability to translate ambiguous business problems into structured technical architectures and present them clearly to diverse audiences. Preferred Qualifications
Certifications: Google Cloud Professional Cloud Architect or Professional Machine Learning Engineer.
Advanced AI Orchestration: Deep exposure to designing architecture for autonomous tool-use patterns and frameworks such as Google ADK, LangChain, or LlamaIndex.
Data Retrieval: Experience integrating and scaling enterprise vector databases (e.g., Vertex AI Vector Search, AlloyDB, Pinecone) for high-performance, embedding-based retrieval.
Regulated Industries: Experience working in healthcare, insurance, or finance, with a strong understanding of data privacy frameworks (e.g., HIPAA) and compliance requirements in solution design.
#LI-HYBRID
Minimum Education Requirements:
High school degree or equivalent required unless otherwise noted above
Location Boston Time Type Full time
Salary Range: $215,010.00 - $262,790.00
The job posting range is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee’s pay position within the salary range will be based on several factors including, but limited to, relevant education, qualifications, certifications, experience, skills, performance, shift, travel requirements, sales or revenue-based metrics, and business or organizational needs and affordability.
This job is also eligible for variable pay.
We offer comprehensive package of benefits including paid time off, medical/dental/vision insurance, 401(k), and a suite of well-being benefits to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, or any other form of compensation that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
WHY Blue Cross Blue Shield of MA?
We understand that the confidence gap and imposter syndrome can prevent amazing candidates coming our way, so please don’t hesitate to apply. We’d love to hear from you. You might be just what we need for this role or possibly another one at Blue Cross Blue Shield of MA. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be brilliant. We encourage you to bring us your true colors, , your perspectives, and your experiences. It’s in our differences that we will remain relentless in our pursuit to transform healthcare for ALL.
As an employer, we are committed to investing in your development and providing the necessary resources to enable your success. Learn how we are dedicated to creating an inclusive and rewarding workplace that promotes excellence and provides opportunities for employees to forge their unique career path by visiting our Company Culture page. If this sounds like something you’d like to be a part of, we’d love to hear from you. You can also join our Talent Community to stay “in the know” on all things Blue.
At Blue Cross Blue Shield of Massachusetts, we believe in wellness and that work/life balance is a key part of associate wellbeing. For more information on how we work and support that work/life balance visit our " How We Work " Page.
Salary Context
This $215K-$262K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Blue Cross Blue Shield of Massachusetts, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($238K) sits 13% above the category median. Disclosed range: $215K to $262K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Blue Cross Blue Shield of Massachusetts AI Hiring
Blue Cross Blue Shield of Massachusetts has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $214K - $262K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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