Title: Sr AI Software Developer
Location: Remote
Duration: Long Term
Client: HHS
Experience: 10+ Years
MOI: Virtual
Job Description Summary
The Software Developer is a hands-on software and AI engineer responsible for modernizing mission-critical legacy applications while maintaining business continuity and interoperability with existing systems. The developer designs, builds, tests, and deploys 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.
What You Will Do:
We are seeking a mid-level Software Developer with 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. This individual 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.
- 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, network paths, and deployment environments.
- Document code design, data models, mappings, interface contracts, migration decisions, test evidence, deployment procedures, and operational runbooks.
- Collaborate in Agile delivery with architects, developers, data engineers, DevSecOps, security, vendors, product teams, and Federal stakeholders.
What You Will Need:
- Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST"; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding with Guidehouse. Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred.
- Bachelor's degree required.
- Minimum TEN (10) years of experience in software development, including legacy application enhancement or modernization, backend service development, data integration, and production support.
- Strong hands-on proficiency in one or more modern programming languages and associated frameworks, such as Python, Java, JavaScript/TypeScript, C#/.NET, Go, or comparable technologies, along with SQL, RESTful API development, automated testing, and maintainable software design.
- Experience engineering AI-enabled applications using foundation-model APIs or machine-learning services, including prompt design, retrieval-augmented generation, embeddings, vector search, evaluation, guardrails, and production monitoring.
- Ability to integrate AI services with enterprise applications and governed data while applying security, privacy, data-use, traceability, human-review, and responsible AI controls throughout the solution lifecycle.
- Experience analyzing and modifying legacy code, extracting business rules, resolving technical debt, and delivering incremental modernization while legacy and modern systems coexist.
- Experience developing AWS-based applications using services such as API Gateway, Lambda, ECS/EKS, SQS, SNS, EventBridge, Step Functions, DynamoDB, CloudWatch, and related integration and observability capabilities.
- Experience implementing secure integrations using OAuth2, OpenID Connect (OIDC), SAML, JWT, or similar authentication and authorization technologies.
- Strong understanding of enterprise integration patterns, API design, event-driven architectures, messaging systems, and asynchronous communication using technologies such as SQS, SNS, EventBridge, Kafka, or similar platforms.
- Solid experience with relational databases such as Oracle, including data modeling, query development, SQL optimization, transactions, troubleshooting, and performance tuning.
- Working knowledge of NoSQL databases such as DynamoDB, MongoDB, Cassandra, or similar modern distributed data stores.
- Ability to design and develop domain models and implement domain-driven design (DDD) principles for complex business applications.
- Experience building an integrated data layer using reusable data services, canonical data models, API and event contracts, mapping, transformation, validation, synchronization, lineage, and reconciliation techniques.
- Experience developing, testing, troubleshooting, and supporting distributed applications across APIs, databases, cloud services, and legacy systems.
- Familiarity with CI/CD pipelines, automated testing, Git-based source control, DevSecOps practices, SDLC processes, agile delivery, and release management.
- Ability to interpret architecture guidance, interface specifications, API contracts, data mappings, user stories, acceptance criteria, and technical design documentation.
- Strong analytical and problem-solving skills with the ability to diagnose and resolve complex integration and interoperability issues across applications, APIs, data flows, authentication or authorization mechanisms, databases, cloud services, and deployment environments.
- Strong documentation skills, including the ability to maintain implementation notes, API documentation, interface specifications, data mappings, test evidence, and support handoff materials.
- Ability to work collaboratively with Solution Architects, developers, DevSecOps engineers, data teams, vendors, security teams, Product Owners, Scrum Masters, Federal stakeholders, and business stakeholders in an Agile environment.
- Self-motivated, adaptable, and able to operate effectively in fast-paced, dynamic environments with appropriate guidance from technical leads.
- Experience provisioning AWS resources through infrastructure as code and deploying application changes through automated CI/CD pipelines.
- Ability to troubleshoot integrations across application code, APIs, AWS services, network paths, identity controls, databases, and deployment environments.
What Would Be Nice To Have:
- Experience supporting platform modernization initiatives within enterprise or government environments.
- Experience supporting federal or government IT programs involving legacy system modernization, shared services, interoperability, cloud adoption, data governance, or phased capability delivery.
- Experience operating in regulated AWS environments, including FedRAMP, NIST 800-53, FIPS, audit logging, and AWS GovCloud where applicable.
- Experience with API documentation tools, API gateways, data integration tools, ETL/ELT patterns, message queues, workflow automation, or integration monitoring dashboards.
- Cloud, agile, development, data, or security certification preferred, such as AWS Certified Developer, AWS Certified Solutions Architect Associate, SAFe, Certified ScrumMaster, Security+, or similar credentials.
- Experience operating in a forward-deployed or customer-embedded engineering model, working directly with mission users, product owners, and subject-matter experts to clarify ambiguous problems and deliver usable solutions rapidly.
- Ability to move from technical discovery and prototyping to production hardening, deployment, documentation, training, and transfer to long-term support teams.
- Experience facilitating technical demonstrations, gathering user feedback, and iterating on capabilities in short delivery cycles.
- Experience prototyping or integrating generative AI capabilities using AWS Bedrock or comparable enterprise AI platforms.
- Understanding of privacy, security, data-use restrictions, and responsible AI considerations in federal or regulated environments.
- Experience with AI evaluation, prompt and model versioning, guardrails, monitoring, human-in-the-loop review, and mitigation of hallucination or unsafe output risks.
- Experience using generative AI and AI-assisted development tools to accelerate the software development lifecycle, including requirements analysis, solution design, code generation and modernization, automated testing, documentation, code review, troubleshooting, and release readiness, while applying appropriate security, privacy, governance, and human-review controls.
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