Senior Software Development Engineer (Remote)

Govcio LLC•UNAVAILABLE, UNAVAILABLE

3d•Remote

About The Position

GovCIO is currently hiring for a Senior Fullstack Engineer to design, build, and deliver modern federal applications with intuitive user interfaces, AWS cloud backends, and AI-powered features. This is a hands-on senior engineering position: the majority of time is spent designing and writing production software on AWS and AI-driven systems, with additional responsibility for mentoring engineers at earlier career stages and contributing to technical direction. Prior experience delivering software for the Department of Veterans Affairs (VA) is required. This position will be located in the Continental United States (CONUS) and will be remote.

Requirements

Bachelor’s Degree with 8 - 12 years of relevant experience, or commensurate experience.
VA experience required. Prior hands-on experience delivering software for the Department of Veterans Affairs (VA).
Clearance Required: Ability to obtain and maintain a Public Trust / VA Tier 2 background investigation.
Demonstrated Ability to work with Public Trust: handle sensitive and confidential information appropriately, including private personal and health information (PII/PHI).
Demonstrated delivery ownership. Track record of taking features or systems from requirements through production and supporting them in operation.
Proficiency with Git, GitHub, and JIRA, including branching strategy, review workflow, and release practices.
Strong system design, application programming, and "clean code" skills. Produces reviewable code changes, reviews the work of others substantively, creates architecture documentation and diagrams independently, and works as a peer to architects.
Deep hands-on AWS engineering experience. Day-to-day development across AWS services, including serverless and event-driven architectures (Lambda, Step Functions, EventBridge, SQS, SNS), data and search services (S3, DynamoDB, Aurora PostgreSQL, Athena, Glue, OpenSearch), and AI/ML services (Bedrock, SageMaker, Textract), with infrastructure defined in CloudFormation.
Fundamental understanding of statistics and foundational machine learning and AI concepts, including distributions, sampling, statistical significance, supervised and unsupervised learning, overfitting, standard evaluation measures, and class imbalance.
Deep understanding of large language models (LLMs) and vision language models (VLMs), including transformer fundamentals, differences between model families, tradeoffs among prompting, retrieval, and fine-tuning, and the choice between managed foundation models and self-hosted or fine-tuned open-weight models.
IDP domain knowledge, including when to apply OCR, OMR, LLMs, and VLMs; data dictionaries and extraction schema design; and quality assurance practices covering ground truth, regression testing, drift detection, and measure selection.
Effective and responsible use of agentic AI coding tools, including specification-driven prompting, verification and testing of generated output, and judgment about appropriate use.
Ability to codify what works into runbooks, reusable assets, and repeatable processes used by other engineers and teams.
Clear written and verbal communication, including the ability to explain technical concepts and decisions to audiences with a wide range of technical backgrounds, up to and including customer stakeholders.
Proficiency in Scrum and Agile, including estimation, planning, and definition and refinement of backlog items in JIRA.
Mentorship and team leadership, including pairing, code review, coaching, and delegation that builds capability.
Initiative and resourcefulness, including the ability to identify needed work and drive it, and to engage peer engineers, product managers, architects, and stakeholders without direction.

Nice To Haves

AWS certification. Existing AWS certification is preferred. Candidates without current certification are expected to earn a Foundational-level certification within the first three months and a first Associate-level certification within the first eight months, and to maintain two or more Associate-level certifications thereafter.
Bachelor’s or Master’s Degree in Computer Science, or in Science, Technology, Engineering, or Mathematics (STEM) fields.
Current AWS certification at the Associate level or above, particularly Professional or Specialty.
Experience fine-tuning, hosting, or benchmarking open-weight models, including work on Amazon SageMaker.
Coursework or professional experience in statistics, machine learning, or data science.
Experience with document processing, OCR, or intelligent document processing (IDP) at scale.
Experience building shared tooling, reusable components, or engineering enablement assets used by other teams.
Experience with reporting and analytics stacks such as Redshift, Athena, or Power BI.

Responsibilities

Designs, architects, and codes applications using a React/Redux frontend and a Python backend built on AWS Lambda Powertools and Boto3, working with AWS services such as API Gateway, Athena, Aurora PostgreSQL, Bedrock, CloudFormation, CloudFront, CloudWatch, Cognito, DynamoDB, ECS, EventBridge, Glue, IAM, KMS, Lambda, OpenSearch, RDS, S3, SageMaker, SNS, SQS, Step Functions, Textract, and VPC.
Builds and delivers Intelligent Document Processing (IDP) systems handling large volumes of digital and scanned documents, including classification, extraction, and validation workflows built on generative AI foundation models.
Owns significant portions of the solution end to end, and partners with architects and other senior engineers on integrations with adjacent systems.
Makes and documents design decisions, and is accountable for the purpose, constraints, and tradeoffs behind them.
Designs and refines prompt and context engineering strategies, and defines how extraction results are evaluated against ground truth.
Defines and maintains extraction schemas and data dictionaries, and keeps them aligned with downstream systems.
Maintains and modernizes existing applications, including operational programs and procedures.
Analyzes and specifies systems factors, including input and output requirements, information flow, hardware and software requirements, and alternative methods of problem resolution, and recommends the approach.
Identifies, prioritizes, and drives down technical debt, including dependency upgrades and adoption of new platform capabilities.
Implements and upholds a rigorous testing approach across unit, integration, and performance testing, and uses cloud-native monitoring and logging to diagnose production behavior.
Implements quality assurance practices for AI-based extraction, including ground truth management, regression testing, and drift detection.
Evaluates and recommends model approaches across managed foundation models, vision language models, and self-hosted or fine-tuned open-weight models on Amazon SageMaker.
Participates substantively in code reviews as a regular reviewer.
Authors and maintains design documentation, program documentation, operations documentation, runbooks, and user guides.
Designs and enforces controls protecting private personal and health information (PII/PHI) across document intake, extraction, and storage.
Contributes to a cross-team center of excellence for AI-assisted engineering, including runbooks, reusable skills and prompt assets, shared tooling, and engineering processes adopted by other teams.
Uses agentic AI coding tools such as Claude Code with a specification-driven approach, and helps establish review and verification expectations for generated code.
Mentors junior and mid-level engineers through pairing, code review, and coaching, and guides their certification and learning paths.
Partners with database and cloud engineers on integration and migration efforts.
Works within and helps lead a multi-disciplined team including architects, human-centered designers, frontend specialists, DevOps engineers, and other software engineers.
Works with Product Owners and Scrum Teams on requirements decomposition, backlog refinement, estimation, and breakdown of user stories and tasks.
Troubleshoots complex issues that span systems and teams, and communicates status and risk to stakeholders.