Senior Manager – Applications Development

Marsh McLennan

Senior AI Applications Development Manager building production agentic systems for Marsh, a global risk, reinsurance, and management consulting firm. Leading architecture, delivery, governance, and optimization across AI platforms.

Posted 8/7/2026full-timePune • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing multi-agent AI architectures, integrating AI systems with enterprise platforms, and leading complex technical delivery across the software development lifecycle. Proficient in building production-grade AI services in Python and applying optimization techniques for scalable digital products.

Highest-signal resume keywords

Multi-Agent AI Architecture DesignProduction-Grade AI Services in PythonLangChain and LangGraph ProficiencyCI/CD Implementation for Agent CodeGovernance and Compliance Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Agentic ArchitectureTool-Calling AgentsFunction-Calling AgentsMemory-Enabled AgentsDSPy ApplicationPrompt EngineeringAutomated TestingBehavioral Drift MonitoringVector Database DesignAPI Integration

Soft Skills

Excellent Communication SkillsGood Troubleshooting SkillsAbility to Work Independently

Tools & Technologies

LangChainLangGraphOpenAIAnthropicLLM PlatformsWorkflow EnginesEnterprise Data Platforms

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's in Data Science

Industry Keywords

Financial ServicesGovernance ExpectationsAuditabilityExplainabilityPrivacy and Security

Tech Stack

Tools & technologies

PythonSDLC

About the role

Key responsibilities & impact

Lead US/Canada payroll projects and work on multiple assignments
Design, build, and scale AI-enabled applications and agentic systems in production
Build multi-agent architectures using planner, executor, reviewer, critic, and supervisor patterns
Develop tool-calling, function-calling, and memory-enabled agents
Orchestrate workflows using LangChain, LangGraph, and similar frameworks
Apply DSPy, prompt engineering, and optimization techniques
Build production-grade AI services in Python
Integrate Anthropic, OpenAI, and open-source LLM platforms
Design RAG solutions using vector databases
Integrate AI systems with enterprise platforms through APIs, workflows, events, and data services
Implement observability, evaluation, tracing, decision logging, and performance monitoring
Work within governance, security, privacy, and compliance frameworks
Lead complex technical delivery across architecture, engineering, governance, and the software development lifecycle
Collaborate with engineering, product, business, risk, and compliance teams
Define agent behavior specifications, prompt and policy versioning, and offline evaluation frameworks
Implement automated testing, guardrails, controlled rollout, telemetry-driven improvement, and continuous optimization
Implement CI/CD for agent code, prompts, and policies
Design human-in-the-loop feedback mechanisms
Monitor and optimize cost, latency, throughput, and behavioral drift
Produce documentation for compliance, internal audit, and regulatory review
Mentor teams in modern AI development practices

Requirements

What you’ll need

Bachelor's degree in Computer Science or Master's in Data Science, related field, or equivalent experience
10+ years of overall technology experience
Experience delivering scalable, resilient digital products
Expertise in agentic architecture and platform design
Ability to design and implement modern multi-agent AI architecture
Experience building planner, executor, reviewer/critic, and supervisor agent patterns
Experience developing tool-calling, function-calling, and memory-enabled agents
Proficiency with LangChain and LangGraph
Experience applying DSPy and prompt optimization techniques
Ability to define reusable agent contracts, interfaces, and schemas
Experience designing hierarchical, collaborative, and event-driven agent workflows
Experience leading delivery across the full software development lifecycle, including an Agentic SDLC
Experience creating agent behavior specifications, prompt and policy versioning, and offline evaluation frameworks
Experience implementing automated testing for agent workflows and guardrails
Experience with controlled rollout, telemetry-driven improvement, and continuous optimization
Proficiency building production-grade AI services primarily in Python
Experience integrating LLMs, vector databases, APIs, workflow engines, and enterprise data platforms
Experience implementing CI/CD for agent code, prompts, and policies
Experience with tracing, decision logging, tool monitoring, and failure analysis
Experience with hallucination detection, consistency, and fitness scoring
Ability to design human-in-the-loop feedback mechanisms
Experience monitoring and optimizing cost, latency, throughput, and behavioral drift
Knowledge of financial services and enterprise governance expectations
Ability to ensure auditability, explainability, privacy, security, and resilience
Ability to embed guardrails against hallucination risk, bias, unauthorized actions, and escalation failures
Excellent verbal and written communication skills
Good troubleshooting and technical skills
Ability to work independently

Benefits

Comp & perks

Professional development opportunities
Interesting work
Supportive leaders
Vibrant and inclusive culture
Range of career opportunities
Benefits and rewards to enhance well-being
Flexible work environment
Flexibility of working remotely
Collaboration, connections and professional development benefits of working together in the office