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About This Role
*Capnexus* is a comprehensive services provider. Our team consists of outstanding professionals, highly experienced in designing, building, and supporting retail software. We see ourselves as a build\-as\-a\-service provider who follows a repeatable business pattern that can be applied to a variety of platforms and verticals. Having a culture built on outcomes and delivery at the core of the business, Capnexus is providing its customers with a complete suite of services for software development, system analysis, integration, implementation, and support, as well as the option to engage a single team to perform all the services they require.
Who You Are and What You'll Do:
*Capnexus* is looking for a highly skilled Senior AWS Data/ML Engineer to lead data architecture, pipeline development, and data integrations. This is an exciting opportunity to apply advanced cloud data engineering skills on a platform that leverages generative AI to automate and modernize enterprise workflows.
Responsibilities:
- Participate in data discovery workshops to inventory source systems including property management platforms, marketing channels, and CRM data, and translate findings into data lake architecture requirements.
- Design and implement a multi\-zone enterprise data lake on Amazon S3 (raw, conformed, enriched, aggregated) with ingest, cleansing, and business layers aligned to the SOW architecture.
- Build batch and streaming data ingestion pipelines using AWS Glue, Amazon Kinesis, and AWS Data Pipeline across CDP, marketing, and property management data sources.
- Implement data transformation and orchestration frameworks using AWS Glue ETL and AWS Step Functions, including AWS Glue Data Catalog for metadata management and discovery.
- Configure Amazon Athena for serverless SQL querying across the data lake; support QuickSight integration with curated data sets for business analytics.
- Develop and deploy ML models on Amazon SageMaker for lead scoring, predictive maintenance, intelligent underwriting risk scoring, and AI\-powered audience segmentation.
- Integrate Amazon Bedrock foundation models to enable generative AI capabilities including customer profile enrichment, hyper\-personalization, and intelligent marketing automation.
- Use Kiro CLI to accelerate AI\-assisted development workflows, spec\-driven pipeline implementation, and automated code generation tasks.
- Design and implement entity resolution pipelines using Amazon Entity Resolution to identify, deduplicate, and merge customer records into unified golden records.
- Implement real\-time and batch data synchronization pipelines between source systems and the Customer Data Platform (CDP).
- Support Azure data lake migration: conduct discovery, assess schemas and transformation logic, provision AWS target environments, execute migration via AWS DataSync, and perform data validation and reconciliation.
- Implement data lake security using AWS Lake Formation, including row\-level security and column\-level encryption.
- Build and maintain data models to support Customer 360 views, ML feature stores, and executive analytics dashboards.
- Ensure data quality, validation, and integrity across all pipeline stages and ML model outputs; support UAT for data\-dependent features.
- Collaborate with Full Stack, DevOps/MLOps, and AWS engagement teams; contribute to architecture documentation, pipeline runbooks, and data governance documentation.
Qualifications:
- 5\+ years of data engineering or ML engineering experience, with at least 2\+ years in AWS cloud environments.
- Strong proficiency in Python and SQL; experience with AWS data services including S3, Glue, Athena, Kinesis, and Step Functions.
- Hands\-on experience with Amazon SageMaker for model development, training, tuning, and endpoint deployment.
- Working knowledge of Amazon Bedrock for integrating and applying foundation models in production\-grade pipelines.
- Experience designing and implementing multi\-zone data lake architectures on Amazon S3, including lifecycle policies and Lake Formation governance.
- Familiarity with Kiro CLI or comparable AI\-assisted/agentic development tooling.
- Experience with entity resolution, deduplication, or master data management concepts and tools.
- Solid understanding of data modeling, feature engineering, data quality practices, and ML integration testing.
- Experience with AWS Lambda and AWS Step Functions for serverless workflow orchestration.
- Familiarity with Amazon API Gateway for exposing data services and model endpoints.
- Strong analytical, problem\-solving, and communication skills; comfortable working in Agile/Scrum teams alongside AWS Professional Services.
Nice to Have:
- Experience with Azure Data Lake, Azure Data Factory, or Azure Synapse — particularly in cloud\-to\-cloud migration contexts.
- Familiarity with Amazon Entity Resolution for customer identity and deduplication use cases.
- Experience with MLOps practices including model monitoring, drift detection, and automated retraining on SageMaker.
- Experience with LLM prompt engineering, RAG architectures, or fine\-tuning workflows on Amazon Bedrock.
- Knowledge of Amazon QuickSight for analytics dataset preparation and embedded dashboard development.
- AWS Certification (Machine Learning Specialty, Data Analytics Specialty, or Solutions Architect).
- Background in real estate, property management, marketing technology, or insurance industries.
"Our Culture":
At Capstone, the central principles that we all adhere to, and the glue that holds us together, are our keystones. Our four keystones are:
"A Customer Obsessed, Delivery Focused, Culture"
- We're driven to exceed our customers' expectations by listening, leading, solving problems, and delivering what we promise
- We aim to be the most dependable and trusted partner serving our customers. TRUST \= CONSISTENCY x TIME
"A Culture of Learning and Sharing"
- We value "Lifetime Learners"; those who are hungry, competitive, curious, and self\-motivated in their pursuit of knowledge.
- Personal and professional growth depends on teamwork and continuous learning. By sharing knowledge, skills, ideas, and effort, we benefit our customers, ourselves, and our communities.
- We recognize that the thoughts, feelings, and backgrounds of others are as important as our own. Everyone has something to learn and everyone has something they can teach.
- Knowledge and ability are valued. Sharing knowledge and helping others learn new capabilities is valued exponentially.
"A Culture of Growth and Scalability"
- Growth comes from not establishing barriers in your role. "Cross functional skill sets are valued and help us deliver to our customers in a truly agile fashion. It comes with understanding that when asked to do something new, you will need support, have questions, and make some mistakes along the way.
- The most elegant solution is a simple solution. Simple doesn't mean easy. It's often more difficult to break a complex problem down into simple, scalable terms. We don't appreciate, or value, over architected solutions or superfluous coding.
- Time is one of our most precious commodities. Scalability implies being respectful of this and passionate about making the most efficient use of each and every one of our team members time.
"All Work is Strategic"
- No matter how small a project or assignment appears, every single engagement is an opportunity for us to prove ourselves, build trust, and develop relationships that last and grow
- Every task, interaction, and commitment matters
- Big or small, we execute our plans and strategies with focus, commitment, and passion
We offer:
Job Type: Full\-time, 1099
Benefits:
- Remote work
*Capnexus* is an equal opportunity employer. We embrace and celebrate diversity and are committed to creating an inclusive and safe environment for all employees. Experience comes in many forms, and we're dedicated to adding new perspectives to the team. We encourage you to apply even if your experience doesn't perfectly align with what we have listed. We look forward to hearing from you.
No Agencies Please!
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 Capnexus, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Capnexus AI Hiring
Capnexus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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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