Software Engineering Manager, AgentOps

Pearson VUE

Software Engineering Manager leading Pearson’s enterprise AgentOps platform in Bangalore. Building reliable, observable, governed agentic AI systems while hiring and developing a high-performing engineering team.

Posted 8/11/2026full-timeBangalore • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading and developing engineering teams while driving the performance and growth of agentic AI systems. Proficient in building LLM-powered applications, ensuring reliability, observability, and compliance within production environments.

Highest-signal resume keywords

Team LeadershipLLM-Powered Systems DevelopmentPython ProgrammingAgent Orchestration ExperienceCloud Platform Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonBackend EngineeringCloud-Native SystemsAgent OrchestrationMulti-Agent FrameworksLLM ObservabilityProductionizing ApplicationsAI Observability FrameworksContext EngineeringHITL Agent Execution

Soft Skills

CommunicationStakeholder Engagement

Tools & Technologies

AWSAzureGCPCrewAILangGraphLangfuseLangSmithArize PhoenixCursorClaude Code

Certifications & Qualifications

Bachelor's in Computer ScienceMaster's in AI/ML

Industry Keywords

Agent DevelopmentOrchestrationObservabilityPsychological SafetyContinuous ImprovementPerformance TargetsRisk ControlsData PrivacyAcceptance CriteriaOperational Risk

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

Hire, coach, and develop a team of AgentOps Forward Deployed Engineers, owning performance, growth, and career progression
Foster an inclusive, high-performing culture of continuous improvement, engineering craft, and psychological safety
Set goals, run delivery cadence, and remove blockers so the team ships with quality and speed
Lead design reviews and set technical patterns and standards
Take high-impact agentic AI use cases from prototype to production using reusable connectors, shared services, and clean crew hand-offs
Establish standardized practices for agent development, orchestration, and LLMOps
Instrument agent crews with end-to-end observability for traces, cost, tokens, latency, and error rates
Own reliability, performance, and cost targets for production agentic workloads
Establish guardrails, constraints, risk controls, and acceptance criteria for agent-driven work
Own agent and connector identity, authentication, credentials, RBAC, and audit models
Ensure agent crews meet regulatory, security, and data-privacy requirements
Manage intake, prioritization, and cross-team dependencies
Translate business needs into clear acceptance criteria
Partner with product, research, cloud, data science, and engineering leaders across OCTO
Advise senior leadership on agentic AI capabilities, trade-offs, and operational risk
Evaluate emerging agent orchestration, multi-agent framework, and LLMOps tools and patterns
Pilot tools and patterns that improve platform capability, reliability, and developer experience

Requirements

What you’ll need

Has led, coached, or mentored engineers as a manager, tech lead, or team lead and is ready to own hiring, performance, and growth
Builds LLM-powered systems, AI agents, or workflow automation in production and remains hands-on with code
Strong Python and backend/platform engineering experience with cloud-native, distributed systems
Experience with agent orchestration or multi-agent frameworks such as CrewAI, LangGraph, or ADK
Experience with LLM observability tooling
Expertise in at least one public cloud platform: AWS, Azure, or GCP
Understanding of the end-to-end agent lifecycle, including orchestration, identity/authentication, observability, guardrails, and reliable production operations
Ability to design with trade-off awareness across quality, latency, cost, resilience, and maintainability
Excellent communication and stakeholder-engagement skills
Bachelor's or Master's in Computer Science, AI/ML, or a related field, or equivalent practical experience
Experience productionizing agentic applications
Familiarity with MCP, A2A, context engineering, and HITL agent execution
Familiarity with AI observability and evaluation frameworks such as Langfuse, LangSmith, or Arize Phoenix
Fluency in AI pair-programming tools such as Cursor or Claude Code

Benefits

Comp & perks

Work on cutting-edge agentic AI systems at enterprise scale
Help build a symbiotic workforce of humans and AI agents
Opportunity to shape next-generation AI engineering practices
Room to stay technical while growing as a people leader
Opportunities to shape strategy and influence outcomes across OCTO
Access to cutting-edge tools, platforms, and thought leadership