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About This Role
### AWS Cloud AI Engineer
- 49920
- Remote, United States
- Full Time
POSITION SUMMARY:
The AWS Cloud AI Engineer 2 at Boston Medical Center (BMC) is responsible for the engineering, implementation, and operational management of secure, scalable AI/ML platforms on Amazon Web Services. This position serves as a Subject Matter Expert (SME) in optimizing the underlying AWS ecosystem, leveraging Infrastructure as Code (IaC) and advanced monitoring to ensure model endpoints and data planes remain highly available. Beyond core cloud engineering, the role focuses on the end\-to\-end operationalization of modern AI and Generative AI workloads. Responsibilities include architecting the infrastructure guardrails necessary for high\-performance environments such as Amazon Bedrock, SageMaker, and Kendra while maintaining strict adherence to enterprise security and governance standards. The ideal candidate will bring strong expertise in AWS architecture, infrastructure automation, DevOps practices, and AI platform integration, along with excellent communication skills and the ability to build strong working relationships across technical and business teams.
Position: AWS Cloud AI Engineer
Department: ITS Network \- Tech Support
Schedule: Full Time
ESSENTIAL RESPONSIBILITIES / DUTIES:
The AWS AI Engineer 2 at Boston Medical Center (BMC) is responsible for the following tasks:
- Engineer, implement, and manage secure, scalable AI/ML platforms specifically within the AWS ecosystem.
- Serve as a Subject Matter Expert (SME) in optimizing AWS infrastructure using Infrastructure as Code (IaC) to ensure high availability for model endpoints and data planes.
- Lead the end\-to\-end operationalization of modern AI and Generative AI workloads, including LLM\-powered applications, Retrieval\-Augmented Generation (RAG), and Agentic AI frameworks.
- Build and maintain reliable, cost\-efficient platforms utilizing native AWS services and automated CI/CD pipelines to transition intelligent solutions from development to production.
- Implement advanced monitoring solutions to oversee platform health, performance, and the stability of AI\-driven workloads.
- Act as a technical lead to advance the organization’s cloud maturity, ensuring all AWS\-based AI solutions are robust, secure, and "AI\-ready."
JOB REQUIREMENTS
REQUIRED EDUCATION AND EXPERIENCE:
- Bachelor’s degree in Computer Science, Engineering, or related discipline with at least 5 years of experience in IT Systems Engineering or equivalent combination of education and experience.
- Demonstrated familiarity with deploying and operationalizing AI\-driven workloads, specifically utilizing services like Amazon SageMaker or Amazon Bedrock.
- Healthcare domain knowledge and working in regulated environments is a plus (HIPAA, HITRUST, SOC2\)
PREFERRED EDUCATION AND EXPERIENCE:
- Master’s degree in Computer Science with a minimum of 5 years of dedicated expertise in engineering and operating enterprise\-scale environments exclusively on AWS.
- 3 years of hands\-on experience managing foundational AWS services (S3, EC2, RDS, VPC, KMS, SNS).
CERTIFICATIONS, LICENSES, REGISTRATIONS PREFERRED:
- AWS Certifications: AWS certified Machine Learning Engineer or AWS certified Generative AI Developer
KNOWLEDGE, SKILLS \& ABILITIES (KSAs):
- Proven experience building and supporting Generative AI solutions, including the integration of Large Language Models (LLMs), foundation models, and the application of advanced prompt engineering techniques to optimize application workflows.
- Familiarity with Retrieval\-Augmented Generation (RAG) and Agentic AI frameworks, specifically orchestrating multi\-step reasoning workflows and integrating LLMs with enterprise vector search capabilities.
- Deep technical proficiency within the AWS AI/ML ecosystem, specifically leveraging Amazon Bedrock, SageMaker, Kendra, and specialized services such as Comprehend, Rekognition, or Lex.
- Proficiency in Python\-based machine learning frameworks such as Hugging Face, PyTorch, or TensorFlow to support the development and deployment of intelligent applications.
- Demonstrated ability to collaborate with data scientists, developers, and platform teams to transition experimental AI/ML workloads into production\-ready, enterprise\-grade cloud environments.
- Experience implementing Infrastructure as Code (IaC) using Terraform or CloudFormation to provision and manage high\-performance environments tailored for AI and LLM\-powered workloads.
- Experience designing and managing CI/CD pipelines (e.g., GitHub Actions, AWS CodePipeline) focused on the continuous integration and delivery of AI models and automated agentic workflows.
- Proficiency in building asynchronous, event\-driven architectures for AI processing using AWS Lambda and modern integration patterns.
- Experience leveraging Docker and Amazon EKS to orchestrate containerized AI microservices and scalable inference endpoints.
- Knowledge of monitoring and observability tools, including Amazon CloudWatch and CloudTrail, to ensure the health and performance of AI model endpoints and data planes.
- Ability to embed security, compliance, and governance controls directly into AI infrastructure automation and delivery pipelines.
- Familiarity with enterprise cloud strategy, including multi\-account architectures and the assessment of workloads for cloud migration or modernization initiatives.
- Experience working within Agile environments, maintaining technical documentation and operational runbooks using tools such as Jira and Confluence.
- Strong analytical and troubleshooting skills with a consistent focus on automation, reliability, and the continuous improvement of the AI ecosystem.
Compensation Range:
$89,500\.00\- $130,000\.00
This range offers an estimate based on the minimum job qualifications. However, our approach to determining base pay is comprehensive, and a broad range of factors is considered when making an offer. This includes education, experience, skills, and certifications/licensures as they directly relate to position requirements; as well as business/organizational needs, internal equity, and market\-competitiveness. In addition, BMCHS offers generous total compensation that includes, but is not limited to, benefits (medical, dental, vision, pharmacy), discretionary annual bonuses and merit increases, Flexible Spending Accounts, 403(b) savings matches, paid time off, career advancement opportunities, and resources to support employee and family well\-being.
NOTE: This range is based on Boston\-area data, and is subject to modification based on geographic location.
Equal Opportunity Employer/Disabled/Veterans
According to the FTC, there has been a rise in employment offer scams. Our current job openings are listed on our website and applications are received only through our website. We do not ask or require downloads of any applications, or “apps” job offers are not extended over text messages or social media platforms. We do not ask individuals to purchase equipment for or prior to employment.
Salary Context
This $89K-$130K range is in the lower quartile 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 Boston Medical Center, 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. This role's midpoint ($109K) sits 49% below the category median. Disclosed range: $89K to $130K.
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.
Boston Medical Center AI Hiring
Boston Medical Center has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $130K - $130K.
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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