Interested in this AI/ML Engineer role at ComponentWise Solutions, Inc.?
Apply Now →Skills & Technologies
About This Role
ComponentWise has spent more than two decades delivering mission\-critical technology for commercial clients and federal agencies, including more than 10 years supporting USCIS digital transformation initiatives.
Our teams build and modernize systems that process millions of immigration cases annually. The work is technically complex, highly collaborative, and directly tied to services people rely on every day. Behind every transaction is a person pursuing legal immigration: a family seeking reunification, a professional building a career, or someone taking the defining step toward citizenship. The software we build does not just process cases. It moves lives forward.
We are a minority\-owned small business that values engineering craftsmanship, long\-term ownership, and sustainable delivery. Our engineers stay because they work on meaningful problems alongside experienced teammates who care deeply about quality. Our very high retention rate is not a talking point. It is what happens when people are proud of what they build and who they build it with.
The Role
We are hiring across multiple experience levels, from early\-career engineers to experienced senior contributors. Responsibilities, technical scope, and ownership will scale based on experience and demonstrated capability.
You will join a collaborative team working at the intersection of software engineering and applied machine learning, building AI/ML capabilities for one of the federal government's largest case management ecosystems. The platform includes more than 100 production microservices, petabytes of data, and a broad network of integrations across federal agencies and interagency partners.
This is a hybrid engineering role. You will develop production services and Python libraries that integrate with foundation models through AWS Bedrock or host custom models, and you will also contribute directly to model development, fine\-tuning, evaluation, and monitoring. Depending on experience level, you may extend existing ML\-powered services, design new AI integrations, fine\-tune models for domain\-specific tasks, or lead innovation using the latest foundation models, cloud services, and GenAI technology.
This role is ideal for engineers who enjoy owning solutions end to end — from model experimentation through production deployment and operations — and who want to build reliable AI systems that operate at large scale.
What You'll Do
\- Design, build, and maintain production services and REST APIs that deliver AI/ML capabilities to the broader platform
\- Develop and maintain Python libraries that integrate with LLMs and AWS AI/ML services (Bedrock, Textract, Comprehend) or host and serve custom models
\- Build, fine\-tune, and evaluate machine learning models using frameworks such as PyTorch, Transformers, and XGBoost
\- Design and implement state of the art solutions using evolving GenAI technologies including RAG, vector databases, agents and MCP
\- Create solutions that ensemble custom models with foundation models, including prompt engineering and retrieval\-based approaches
\- Deploy and operate model\-serving workloads on Kubernetes and AWS cloud\-native infrastructure
\- Develop automation for model monitoring, evaluation, and retraining for new and existing solutions
\- Implement GenAI observability solutions to optimize governance, monitoring and cost optimization
\- Support event\-driven and asynchronous AI/ML architectures, including messaging systems such as Kafka and AWS SQS
\- Perform exploratory data analysis on large\-scale data using Python and Spark
\- Write and maintain unit, integration, and end\-to\-end tests for services and Python libraries
\- Contribute to CI/CD pipelines and automated testing workflows
\- Participate in monitoring, troubleshooting, and operational support appropriate to experience level
\- Collaborate with data scientists, data engineers, developers, product owners, and government stakeholders to deliver mission\-critical capabilities
\- Contribute to secure, maintainable, and well\-tested software throughout the development lifecycle
Core Technologies
\- Python, Flask, FastAPI, Spark
\- PyTorch, Transformers, Scikit\-learn, XGBoost, NLP, NER
\- LLMs, foundation models, GenAI, Claude, AWS Bedrock
\- AWS (EKS, Lambda, SQS, S3, Textract, Comprehend, CloudWatch)
\- Databricks, Databricks MLflow, model registry, Delta Lake
\- ANSI SQL, Spark SQL, PostgreSQL
\- Kafka, event\-driven architectures
\- Kubernetes, Helm, ArgoCD
\- GitHub Actions, Harness
\- New Relic, Splunk
\- Pytest, unittest
Core Qualifications
\- Bachelor's degree in Computer Science, Data Science, or a related technical field, or equivalent practical experience
\- Experience building production software and services using Python or another modern programming language
\- Experience developing, training, or fine\-tuning machine learning models using Python
\- Experience integrating applications with APIs, cloud services, or ML model endpoints
\- Hands\-on experience with SQL and data frames (Pandas, Spark, etc.)
\- Understanding of software engineering fundamentals, the machine learning lifecycle, and deployment and monitoring best practices
\- Strong communication and collaboration skills
\- Ability to learn quickly and adapt to evolving technologies and mission needs
\- US citizenship and ability to obtain and maintain DHS suitability
Additional Experience That's Helpful
\- Building and publishing internal Python libraries or SDKs
\- Working with AWS Bedrock, Textract, Comprehend, or other managed AI/ML services
\- Fine\-tuning foundation models or training domain\-specific models for tasks such as text extraction, NER, and NLP
\- Hosting and serving custom models in production (containerized inference, batch scoring, or streaming)
\- Prompt engineering using the latest foundation models
\- Building analytics and ML solutions in Databricks
\- Experience with event\-driven and asynchronous AI/ML systems
\- Developing data\-driven solutions in DataOps and MLOps processes
\- Model monitoring in production and automated retraining
\- CI/CD pipeline development, GitOps workflows, and infrastructure automation
\- Observability and production support
How We Work
\- Small collaborative teams with high ownership
\- PR\-based development and peer code review
\- Automated testing and CI/CD by default
\- Engineers contribute to architecture and operational decisions
\- Sustainable pace over hero culture
\- Focus on maintainability, reliability, and long\-term system health
Engineers at all levels are encouraged to contribute ideas, grow their technical depth, and take on increasing ownership over time. We value curiosity, strong fundamentals, sound judgment, and a willingness to learn as much as prior experience with any specific technology.
Why Join Us
\- Work on systems that operate at global scale
\- Solve technically challenging distributed systems and applied AI problems
\- Collaborate with experienced engineers in a low\-ego environment
\- Build AI/ML solutions with measurable real\-world impact
\- Join a team with strong long\-term retention and meaningful ownership opportunities
Our strongest hires have not always come with every skill on the list. What they shared was a strong engineering foundation, clean code as a habit rather than an afterthought, and the kind of intellectual curiosity that makes hard problems feel like opportunities. If that is how you think, we want to talk.
This position requires the ability to obtain and maintain a DHS suitability determination. US citizenship is required.
Work Location: Remote
Pay: $90,000\.00 \- $150,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Work Location: Remote
Salary Context
This $90K-$150K 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 ComponentWise Solutions, 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 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 ($120K) sits 44% below the category median. Disclosed range: $90K to $150K.
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.
ComponentWise Solutions, Inc. AI Hiring
ComponentWise Solutions, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $150K - $150K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.