Software Engineering Manager

Who you are

• A demonstrated track record of building, shipping, and operating production software, including hands-on delivery of AI-native application features that real users depend on rather than demos
• Direct experience authoring agentic instructions and prompts, designing AI-driven autonomous workflows, and building the evaluation and testing that verifies them, is required
• Experience delivering conversational experiences in chat and voice,
• Willingness to work directly with customers in a forward deployed capacity is required; prior forward deployed experience is an advantage
• Typically requires a minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree
• 5+ years of experience as a technical lead for technical teams
• Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience
• Advanced degrees or certifications are a plus but are not a substitute for a demonstrated record of shipping reliable AI-native applications
• Software engineering fundamentals. Strong command of data structures, algorithms, system design, APIs, data modeling, and testing
• AI native application development. Hands-on experience building applications where model-driven behavior is central — agent orchestration at the application layer, tool and function calling, context assembly, grounding against enterprise data, and handling latency, cost, and failure
• AI-driven autonomous workflow design. Demonstrated experience designing workflows in which agents carry out multi-step business processes with limited supervision: process decomposition, decision points and autonomy boundaries, human-in-the-loop checkpoints, exception and retry handling, and observability over what the agent did and why
• Conversational and multi-channel design. Practical experience across chat and voice, including Voice AI: turn and context management, intent and entity handling, disambiguation and confirmation patterns, automated-to-human handoff, and the latency and speech recognition constraints voice adds
• Agentic instruction authoring. Demonstrated skill writing and maintaining the natural-language logic that governs agent behavior — instructions, role definitions, tool descriptions, guardrails, and refusal and escalation rules — with versioning, review, and regression coverage applied as they would be to code
• Prompt engineering. Intent-driven prompt design: decomposition, golden and few-shot examples, structured output and schema enforcement, grounding and citation, and disciplined iteration against evaluation results rather than impressions
• Evaluation and testing of non-deterministic systems. Ability to build measurable frameworks assessing response quality, agent behavior, tool-selection accuracy, and regression risk — golden datasets, scenario suites, model-as-judge scoring with human calibration, continuous evaluation pipelines, and drift detection — plus adversarial, jailbreak, and grounding testing
• Specification precision and architectural judgment. Ability to define problems rigorously enough that another engineer or an AI agent implements them correctly, and to decide soundly when to solve a problem in code, in instructions, or by delegating to an agent
• Safety, security, and data handling. Working knowledge of risks specific to AI-integrated applications — prompt injection, sensitive-data and secret leakage, over-broad tool access, unsafe autonomous action — translated into concrete guardrails, least-privilege controls, and monitoring
• Experience in managing cross-functional teams with combined engineering and quality responsibilities
• Voice and contact center technology. Telephony and contact center platforms, IVR, speech recognition and synthesis, streaming audio, and real-time latency optimization
• Conversation design partnership. Experience working alongside conversation or content designers, contributing to dialogue flow, tone, and error-recovery design
• Evaluation and observability tooling. Evaluation frameworks, prompt and instruction management tooling, tracing for LLM applications, and analysis of production transcripts at scale
• Enterprise domain depth. Customer service, contact center operations, sales, or enterprise workflow, at a depth sufficient to challenge a requirement rather than only implement it What the job involves
• As the Manager of Software Engineering, you will lead the OmniChannel team within Customer Support Management (CSM)
• This hands-on leadership role will set technical direction, driving execution of key initiatives such as Voice AI, Chat AI, Contact Center Integrations among other Omni Channel capabilities
• In addition you will also oversee engineering operations, ensure product quality and scalability, and foster collaboration across global teams
• As a Manager, Software Engineering Management, you will set technical direction and serve as a thought partner to your engineers and pr