Manager, Software Engineering Omni Channel Management

ServiceNow•Santa Clara, CA

2d•Hybrid

About The Position

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 product counterparts, staying close enough to the work to make sound technical judgments and unblock your team when it matters. You will lead a team as a people manager, investing in their growth deliberately, raising the performance bar with intention rather than simply maintaining it, and consistently creating space for innovation alongside a fast-moving technical growth.

Requirements

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.
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.

Nice To Haves

Advanced degrees or certifications are a plus but are not a substitute for a demonstrated record of shipping reliable AI-native applications.
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.

Responsibilities

Manage product development activities and oversee end-to-end engineering deliverables.
Manage and build a team of engineers by identifying individual strengths, providing career development, and proactively elevating engineers.
Manage daily activities and lead monthly release cycles with product management, providing technical feedback to maintain delivery velocity, ensuring engineering excellence and code quality.
Design conversational experiences across chat and voice. Build experiences that hold context across turns, hand off cleanly between automated and human agents, and behave consistently across channels — accounting for what voice imposes: latency budgets, barge-in, speech recognition error, disambiguation, and confirmation before consequential actions.
Build AI native applications. Design and ship applications built around agentic behavior — intent interpretation, multi-step reasoning, tool invocation, and action on the user's behalf — together with the data models, integrations, and channels that make them usable in production.
Design AI-driven autonomous workflows. Decompose business processes into the steps and decision points an agent can execute — determining where autonomy is appropriate, where a human checkpoint is required, and how exceptions, retries, and hand-back to a person are handled.
Build automated evaluation and test non-deterministic behavior. Design and operate the evaluation that makes change safe — golden datasets, multi-turn conversation suites, model-as-judge scoring calibrated to human review, CI gates, and drift detection — plus adversarial, jailbreak, grounding, and tool-selection testing.
Specify precisely and direct AI coding agents. Convert requirements into testable specifications with explicit scope, constraints, and acceptance criteria; decompose work into agent-sized tasks; and review agent output for correctness and maintainability. You own the result regardless of what produced it.
Own quality, safety, and reliability in production. Monitor conversation quality, containment, hallucination, and unsafe actions; defend against prompt injection and data leakage; and feed production failures back into specifications and evaluation sets.
Collaborate across product, design, and engineering. Partner with product managers, designers, conversation designers, and engineers to define success criteria, align on tradeoffs, and communicate capability and risk clearly.
Solve ambiguous problems and fast-changing priorities by providing clear direction to the team by fostering collaboration and applying structured decision-making.