Modernization Software Developer

About this role

Job Title: Modernization Software Developer

Job Category: Engineering

Time Type: Full time

Minimum Clearance Required to Start: Public Trust

Employee Type: Regular

Percentage of Travel Required: Up to 10%

Type of Travel: Local

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The Opportunity: We are seeking an experienced Software Developer with hands-on software and AI engineering experience. The candidate will be responsible for modernizing mission-critical legacy applications while maintaining business continuity and interoperability with existing systems. The developer will design, build, test, and deploy modern application and AI-enabled capabilities using the programming languages, frameworks, models, and platforms best suited to each solution, such as Python, Java, JavaScript/TypeScript, C#/.NET, Go, or comparable technologies. These capabilities will coexist with legacy applications and databases through an integrated data layer that provides governed, reusable access to shared data through APIs, events, data services, canonical models, transformation components, and AI-ready knowledge services. Working with architects, data engineers, security, DevSecOps, product teams, and vendors, the developer incrementally refactors or replaces legacy functionality, responsibly integrates AI into business workflows, preserves essential business rules, reduces point-to-point dependencies, and delivers secure, maintainable AWS-based solutions.

Responsibilities: As a mid-level Software Developer, utilize AI engineering capabilities to help modernize a large-scale GrantSolutions application portfolio through phased, low-risk delivery. The developer will enhance and refactor legacy applications, build new cloud-hosted services using modern languages and frameworks, and develop production-ready AI capabilities that improve information access, automate workflows, and augment user decision-making. The developer will implement interoperability between modern, AI-enabled, and legacy capabilities through an integrated data layer. The role is primarily a software development position, with responsibility for application code, reusable data and AI services, APIs, events, transformation logic, automated tests, AI evaluation, monitoring, and production support.

The Software Developer will translate architecture and data-governance standards into working software. The developer will expose legacy capabilities safely, develop modern modules and microservices, establish consistent data contracts, and support synchronization and traceability across systems of record. The candidate will collaborate with Solution Architects, Product Owners, application and data teams, vendors, security teams, and Federal stakeholders to deliver incremental modernization without disrupting ongoing operations.  The candidate will: • Analyze legacy applications, data structures, interfaces, dependencies, and business rules to identify safe modernization increments.

• Enhance and refactor legacy applications and databases while developing new APIs, services, automation, and cloud-native components using appropriate modern languages and frameworks, including Python, Java, JavaScript/TypeScript, C#/.NET, Go, or comparable technologies.

• Implement phased modernization patterns that allow modern and legacy capabilities to operate concurrently without disrupting mission operations.

• Build and maintain an integrated data layer that provides consistent, governed access to shared data through APIs, events, reusable data services, canonical models, and transformation components.

• Develop interoperability between legacy applications, modern services, vendor platforms, and analytics capabilities using synchronous APIs, asynchronous messaging, events, batch processing, and change-data patterns.

• Implement data mapping, validation, transformation, lineage, reconciliation, synchronization, error handling, and auditability across systems of record.

• Design, prototype, and productionize AI capabilities such as retrieval-augmented generation, semantic search, document intelligence, classification, summarization, intelligent assistants, and agent-supported workflows when appropriate to the business need.

• Integrate foundation models and machine-learning services with enterprise applications, APIs, and governed data using prompt orchestration, embeddings, vector search, tools, and model-agnostic service patterns.

• Design versioned API, event, and data contracts that decouple consumers from legacy implementation details and support controlled schema evolution.

• Preserve validated business rules and compliance controls while progressively separating tightly coupled application and data components.

• Develop resilient services using retries, timeouts, idempotency, dead-letter queues, transaction controls, and failure-recovery mechanisms.

• Implement secure authentication, authorization, encryption, secrets management, and least-privilege access across application and data interfaces.

• Create unit, integration, contract, regression, and performance tests to verify business behavior and end-to-end interoperability.

• Develop AI evaluation suites and operational controls for accuracy, groundedness, relevance, latency, cost, security, privacy, prompt injection, sensitive-data exposure, model drift, and other use-case-specific risks.

• Implement responsible AI controls, including guardrails, traceability, audit logging, model and prompt versioning, human-in-the-loop review, fallback behavior, and ongoing monitoring aligned with organizational and Federal requirements.

• Use CI/CD pipelines, infrastructure as code, code reviews, automated security scanning, and feature controls to deliver changes safely and repeatedly.

• Instrument application and data flows with centralized logging, metrics, tracing, health checks, alerts, and operational dashboards.

• Troubleshoot defects across application code, data flows, APIs, events, databases, AWS services, identity controls,

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