Machine Learning Engineer

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

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, This position requires the ability to obtain and maintain a DHS suitability determination. US citizenship is required.

$90,000 - $150,000 a year - Full-time, Pulled from the full job description

• Parental leave
• 401(k)
• Health insurance
• Retirement plan
• 401(k) matching
• Paid time off
• Vision insurance, * 401(k)
• 401(k) matching
• Dental insurance
• Flexible schedule
• Health insurance
• Paid time off
• Parental leave
• Retirement plan
• Vision insurance

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.