Technical Lead

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, DevSecOps, 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/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.