Senior Manager, Software Engineering

We’re looking for a Software Engineering Senior Manager to lead the team responsible for scaling and productionizing applications within our federal solutions portfolio. You will partner with AI engineers, software developers, product leaders, and program stakeholders to solve mission-centric problems across a variety of customer environments, from commercial cloud to AWS GovCloud to fully disconnected networks.

Your specific focus will be on building and leading a high-performing engineering team that makes our applications performant, scalable, secure, and durable. You will set technical direction, establish clear ownership and delivery practices, develop engineering talent, and guide solutions through the accreditation gauntlet that stands between working software and software the mission can use in real-world environments.

The Impact

At Seekr, we move fast and value flexible thinkers who embrace a growth mindset. Federal work rewards a specific kind of adaptability: requirements get tweaked in real time, the deployment target changes, user feedback loops run through environments you cannot access directly, and the security posture you designed against gets a new control overlay two weeks before delivery.

Leaders who can help their teams absorb that churn without losing the thread will make an immediate impact here. You will be accountable for how our federal solutions are staffed, built, deployed, and accredited, and for ensuring the team’s decisions hold up for years across multiple programs.

Duties and Responsibilities

• Lead and develop a team of software engineers working closely with AI engineers to ship high-quality, production-ready features
• Own team execution and the path from functional prototype to stable, scalable, accredited service
• Set technical direction, drive sound decision-making, and establish and enforce engineering best practices across the federal engineering team
• Guide the design and delivery of systems that satisfy security controls as a first-class requirement rather than a post-hoc remediation exercise
• Ensure the team builds and maintains hardened container images, CI/CD pipelines, and deployment artifacts for cloud and restricted or air-gapped environments
• Lead incident response and root cause analysis for production issues in environments with limited observability and access
• Partner with Product, Program, Security, and customer-facing leaders to prioritize work, manage delivery risk, and translate requirements into actionable engineering plans
• Hire, coach, and retain engineers; set clear expectations; provide regular feedback; and create development opportunities through code review, design review, pairing, and delegated ownership

Required Qualifications

• 6+ years of professional software engineering experience, including 1+ years leading engineering teams that build and operate large-scale distributed systems
• Bachelor’s degree in computer science or a related field, or equivalent professional experience
• Demonstrated success leading engineers and delivering production systems, with sufficient technical depth to guide architecture, debugging, and operational decisions
• Technical depth and applied experience sufficient to lead teams working in:
• Python (async patterns, packaging, dependency management)
• PostgreSQL or a comparable RDBMS
• RESTful API design (FastAPI preferred)
• Containerization and orchestration (Docker, Kubernetes, Helm)
• Distributed systems (particular emphasis on fundamentals: idempotency, retries, eventual consistency, queueing, failure isolation, graceful degradation, etc.)
• CI/CD (GitLab CI preferred)
• Automated testing (unit, integration, E2E)
• Observability (structured logging, performance metrics, tracing)
• Strong written and verbal communication skills with both technical and non-technical audiences

What We’re Looking for in Practice

• You can explain the tradeoffs behind your team’s architecture decisions, including decisions that did not work and how you helped the team adapt
• You can help a team debug a system it did not build in an environment it cannot reproduce locally, using limited evidence such as logs and a stack trace
• You establish responsible team practices for AI coding assistants (Claude Code, Codex, Cursor, and similar), using them as accelerants while maintaining clear standards for review, testing, security, and ownership of all code that ships
• You have led teams shipping AI-backed features (LLM inference, agents, retrieval), understand where they are appropriate and where they are not, and have the judgment to guide the team toward the simplest solution that solves the problem at hand