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Technical Lead, Software Development, Backend Developer

Job in McLean, Fairfax County, Virginia, USA
Listing for: System One Holdings, LLC
Full Time position
Listed on 2026-09-06
Job specializations:
  • Software Development
    Backend Developer
Job Description & How to Apply Below

Technical Lead

Location:

McLean, VA Type:
Contract-to-Hire Compensation: $ - $ Work Model: 99% remote but prefer candidates in the Washington, DC Metro Security Clearance: US Citizenship and the ability to obtain a public trust

System One is looking for a strong technical lead for a contract-to-hire role in McLean, VA. This position is 99% remote, but the client would like the team to be located in the Washington DC metro in case they need to meet in person.

Responsibilities:
  • Provide hands-on technical leadership for a development team, establishing implementation direction, engineering standards, delivery priorities, and clear accountability for technical outcomes.
  • Lead solution design, technical discovery, estimation, work decomposition, and implementation planning for complex modernization features and releases.
  • Conduct architecture, design, and code reviews; enforce secure coding, testing, documentation, and maintainability standards; and ensure timely remediation of technical debt and quality issues.
  • Mentor developers through pairing, technical coaching, constructive review feedback, reusable patterns, and knowledge sharing that improves team capability and consistency.
  • Identify and communicate technical risks, dependencies, tradeoffs, and escalation needs; coordinate resolution across architecture, security, data, Dev Sec Ops , vendor, and product teams.
  • 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/Type Script, 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 product ionize 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,…
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