Manager, Software Development

What This Role Owns:

Decision rights are explicit so this role is a leader and not a coordinator. The manager owns day-to-day delivery and production decisions for team-owned services within architectural and roadmap guardrails; prioritization execution through the Senior Product Owner; hiring, performance management, and capability development for the engineering team; on-call ownership and incident response for the team's coverage window; and operational tradeoffs involving reliability, feature velocity, platform sustainability, and cost. The manager also serves as the primary distributed team leadership interface with architecture, product, and platform teams.

Key Responsibilities:

• Lead, coach, and develop a distributed software engineering team comprising AI Software Engineers, AI Systems Engineers, and a Senior Product Owner.
• Drive predictable delivery of consumer AI platform capabilities, platform engineering initiatives, and reliability improvements in partnership with onshore engineering and product teams.
• Partner with onshore architects, engineering managers, and product managers to execute roadmap priorities and ensure alignment across distributed teams.
• Support delivery of reusable AI platform capabilities including agent orchestration services, conversational platforms, SDKs, Model Context Protocol (MCP)-enabled integrations, and AI runtime services.
• Foster engineering excellence through software development best practices, code quality, automated testing, CI/CD, and operational discipline.
• Ensure reliability, availability, observability, and operational readiness of production AI platform services.
• Lead hiring, performance management, mentoring, and career development while building a collaborative, high-performing engineering culture.
• Drive incident management, root cause analysis, and continuous improvement initiatives to enhance platform reliability and operational excellence.
• Ensure compliance with security, governance, and software engineering standards while optimizing cloud resources and platform costs.
• Communicate delivery progress, risks, dependencies, and engineering priorities to stakeholders across the organization.

What You'll Bring:

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
• 10+ years of software engineering or platform engineering experience, with at least 3 years specifically in AI platform engineering, agentic AI systems, or LLM-powered service development.
• 5+ years leading engineering teams, including hiring, performance management, delivery accountability, and talent development.
• Demonstrated technical depth in AI platform or distributed systems engineering able to evaluate architecture decisions, identify design flaws, drive technical tradeoffs, and hold engineering teams to a high quality bar without relying on others to translate.
• Experience with cloud-native technologies, including Kubernetes, CI/CD platforms, Infrastructure-as-Code, and modern observability tooling.
• Experience leading distributed or geographically dispersed engineering teams across time zones.
• Experience partnering with Product Owners or Product Managers in Agile delivery environments.
• Strong communication skills and the ability to influence engineering and business stakeholders.
• Experience building software platforms that support consumer-facing AI applications, conversational AI, agentic AI, or LLM-powered services.

Must Have Skills:

• Engineering manager or technical lead with a track record of hiring, coaching, and growing software engineering teams
• Background building or operating AI platforms, ML platforms, or developer platforms that run in production at scale
• Hands-on experience with agentic AI systems, AI agents, chatbots, virtual assistants, or conversational AI products
• Experience shipping LLM-powered applications or generative AI features into production, including prompt design, model integration, and inference optimization
• Strong foundation in distributed systems, microservices, REST or gRPC APIs, and backend software architecture
• Proficiency with cloud-native technologies Kubernetes, Docker, CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), Infrastructure-as-Code (Terraform, Helm), and observability tooling (Datadog, Splunk, OpenTelemetry)
• Experience managing distributed, remote, or globally distributed engineering teams across multiple time zones
• Experience with voice AI, speech AI, or real-time telephony systems including speech-to-text (STT), text-to-speech (TTS), voice bots, IVR, contact center AI, or conversational phone applications

Nice to Have:

• Exposure to Model Context Protocol (MCP), tool-calling frameworks, function calling, or building reusable AI service components
• Familiarity with AI observability, LLM monitoring, model evaluation, or responsible AI and AI governance practices
• Experience with multimodal AI, vision-language models, document AI, or real-time data processing beyond voice
• Background leading cross-functional teams that include engineers, platform specialists, and product managers or product owners working toward a shared delivery roadmap