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About This Role
We are looking for a Data & AI Engineer to join our team and contribute to data engineering, analysis, and AI/ML initiatives within a healthcare technology environment. The ideal candidate has strong experience working with large and complex datasets, data quality and transformation, Python-based data processing, and cloud data platforms.
You will work closely with data, biomedical, IT, and engineering teams to ensure data is reliable, scalable, and ready to support advanced analytics, predictive algorithms, and AI-driven solutions. The focus of this role is data engineering and analysis.
We offer the opportunity to work on data and AI initiatives that make a meaningful impact in healthcare technology. Also, a collaborative environment spanning data, biomedical, IT, and engineering teams.
Location: Cambridge (Massachusetts, US)
Work Mode: on-site
Type of contract: C2C
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Key Responsibilities:
Data Wrangling
- Retrieve data from AWS S3 and query and analyze datasets using Amazon Athena, SageMaker, and Jupyter Notebooks.
- Clean, transform, and prepare large datasets from multiple sources.
- Identify missing data, inconsistencies, time-alignment discrepancies, and artifact-prone measurements.
Data Management
- Develop and maintain data processing pipelines that transform and parse CSV, XML, and other flat-file formats into scalable databases and data structures such as InfluxDB.
- Develop and run Python and Bash scripts to automate ingestion, inventory, validation, and quality assessment of incoming device data.
Data Integrity Evaluation
- Investigate data gaps, anomalies, and integrity issues using Athena, SageMaker, or Jupyter Notebooks.
- Determine whether root causes relate to data curation, technical configuration, system performance, or storage limitations.
Troubleshooting and Collaboration
- Collaborate with biomedical and IT teams at partner sites to troubleshoot data transfer and collection issues and ensure reliable data availability.
- Work closely with internal engineering, data, and technical teams to investigate and resolve data-related issues.
Data Modeling and AI/ML
- Analyze and model physiological, vital-sign, and EMR data from multiple sources to support data-driven and AI/ML solutions.
- Develop, enhance, and evaluate predictive algorithms and machine learning models using multivariate data, including numerical and textual data.
Statistical Validation
- Cross-validate Python-based machine learning and regression results against outputs generated in SAS, in collaboration with biostatistics teams.
Data Simulation
- Prepare and transform retrospective CSV/XML datasets to generate simulated data using a Virtual Hospital Simulator.
- Support algorithm testing and validation within surveillance software.
Continuous Improvement
- Contribute to improving data quality, scalability, reliability, and analytical capabilities across data and AI initiatives.
Required Qualifications:
- 7 to 10 years of professional experience in data engineering and data analysis.
- Hands-on AI/ML experience, including developing, evaluating, and enhancing predictive algorithms and machine learning and regression models using multivariate data (numerical and text-based).
- Experience with healthcare data, including physiological signals, vital signs, and EMR data.
- Background in the medical device industry, including experience working with device-generated clinical data.
- Strong proficiency in Python for data processing, analysis, and machine learning.
- Experience scripting in Bash for batch processing and automation.
- Hands-on experience with AWS data services, including S3, Athena, and SageMaker.
- Experience working in Jupyter Notebooks for exploratory analysis and data investigation.
- Proven experience parsing and transforming flat-file formats (CSV, XML) into scalable databases, with exposure to time-series databases such as InfluxDB.
- Demonstrated ability to identify and resolve data quality issues, including missing data, time-alignment problems, and measurement artifacts.
- Strong troubleshooting skills and the ability to diagnose root causes across data, configuration, and infrastructure.
- Excellent communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders across multiple organizations.
Why choose us
- An international community bringing together more than 110 different nationalities
- An environment where trust is central: 70% of our leaders started their careers at the entry level
- A strong training system with our internal Academy and more than 250 modules available
- A dynamic work environment that frequently comes together for internal events (afterworks, team buildings, etc.)
Amaris Consulting promotes equal opportunities. We are committed to bringing together people from diverse backgrounds and creating an inclusive work environment. In this regard, we welcome applications from all qu**alified individuals, regardless of sex, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.
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Who are we?
Amaris Consulting is an independent technology consulting firm providing guidance and solutions to businesses. With more than 1000 clients across the globe, we have been rolling out solutions in major projects for over a decade – this is made possible by an international team of 7,600 people spread across 5 continents and more than 60 countries. Our solutions focus on four different Business Lines: Information System & Digital, Telecom, Life Sciences and Engineering. We’re focused on building and nurturing a top talent community where all our team members can achieve their full potential. Amaris is your steppingstone to cross rivers of change, meet challenges and achieve all your projects with success.
At Amaris, we strive to provide our candidates with the best possible recruitment experience. We like to get to know our candidates, challenge them, and be able to give them proper feedback as quickly as possible. Here's what our recruitment process looks like:
Brief Call: Our process typically begins with a brief virtual/phone conversation to get to know you! The objective? Learn about you, understand your motivations, and make sure we have the right job for you!
Interviews (the average number of interviews is 3 - the number may vary depending on the level of seniority required for the position). During the interviews, you will meet people from our team: your line manager of course, but also other people related to your future role. We will talk in depth about you, your experience, and skills, but also about the position and what will be expected of you. Of course, you will also get to know Amaris: our culture, our roots, our teams, and your career opportunities!
Case study: Depending on the position, we may ask you to take a test. This could be a role play, a technical assessment, a problem-solving scenario, etc.
As you know, every person is different and so is every role in a company. That is why we have to adapt accordingly, and the process may differ slightly at times. However, please know that we always put ourselves in the candidate's shoes to ensure they have the best possible experience.
We look forward to meeting you!
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 Amaris Consulting, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Amaris Consulting AI Hiring
Amaris Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, 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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