Director, Automation, India

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1. Innovation Strategy & AI Architecture – 25%

Set the multi-year AI and automation innovation roadmap for ReSource Pro—identifying technology bets in agentic AI, LLM orchestration, intelligent document processing, multi-agent systems, and AI-native workflow design that advance the company’s position as an AI-first service provider.

Own solution architecture at the program level: define reference architectures, integration patterns, and platform standards that govern how AI agents, automation tools, cloud services, and data pipelines work together across the enterprise.

Lead the design of AI orchestration architectures (multi-agent systems, RAG pipelines, tool-augmented workflows, hybrid human-AI models) that move beyond traditional RPA into fundamentally reimagined service delivery.

Challenge existing solution patterns with disciplined first-principles thinking—asking whether processes should be redesigned or eliminated before asking how to automate them.

Build and maintain a technology radar that continuously tracks emerging AI capabilities (new foundation models, agentic frameworks, MLOps tooling, synthetic data approaches) and translates findings into quarterly executive briefings on strategic implications.

Lead build-vs-buy-vs-partner evaluations with technical rigor and commercial discipline, ensuring investment decisions are grounded in realistic production deployment timelines, not just demo potential.

Represent ReSource Pro as a technical thought leader at industry conferences, client innovation summits, and executive forums.

2. Solution Design, Velocity & High-Impact Prototyping – 25%

Lead solution design for high-value automation opportunities across insurance operations (policy administration, claims processing, underwriting, billing, document intake)—designing AI-native solutions that meaningfully reduce manual effort, accelerate cycle times, and improve accuracy at scale.

Run a structured, high-velocity innovation lab process: define hypothesis, build minimum viable prototype, execute controlled pilot with defined success criteria, validate production-readiness, and make disciplined go/no-go decisions with executive backing.

Maintain a fast prototype-to-production pipeline—setting team velocity standards that drive solutions from initial concept to deployable product within defined time horizons, without sacrificing technical quality or operational stability.

Ensure every solution exiting the innovation pipeline is documented with clear technical specifications, integration requirements, confidence-tier definitions, and operating procedures—so it can be deployed and maintained without the original inventor in the room.

Lead client-facing discovery workshops and innovation sessions that identify high-ROI automation opportunities—bridging deep technical feasibility assessment with business impact analysis to support Business Development and sharpen proposal quality.

Partner with Data Strategy and Data Services to ensure data pipelines, labeling frameworks, evaluation datasets, and feature stores are production-ready before any AI solution moves to deployment.

Continuously improve solution design quality through post-deployment retrospectives, production performance analysis, and structured lessons-learned loops that feed back into the next generation of solutions.

3. Technical Excellence, FDE Enablement & Platform Maturity – 20%

Build automation and AI solutions to a technical standard that supports a Forward Deployed Engineer (FDE) operating model—meaning solutions are configurable, well-documented, robust to edge cases, and operable by skilled engineers without deep dependency on the original development team.

Drive the technology stack deliberately beyond traditional RPA: architect solutions that combine agentic AI, intelligent document processing, LLM orchestration, and confidence-tiered quality frameworks to handle the complexity and variability of real insurance workflows.

Define and enforce technical quality standards across all solutions: modularity, observability, failure handling, version control, rollback capability, and production monitoring—so that what gets built stays working as volume and complexity scale.

Establish delivery velocity metrics for the innovation team (time-to-prototype, time-to-pilot, time-to-production, reuse rate across engagements) and hold the team accountable to improving them quarter over quarter.

Design hybrid human-AI operating models that define clearly which tasks AI agents own autonomously, where human review is required, and how confidence scoring governs quality assurance—building systems that are trustworthy in production, not just impressive in demos.

Serve as the primary technical escalation point when deployed AI solutions require architectural changes, model updates, or root-cause investigation—maintaining production stability while the team iterates on the next generation.

Facilitate regular technical showcases that demonstrate new platform capabilities to delivery, business development, and client teams—building organizational pull for the innovation pipeline and raising the technical literacy of the broader automation program.

4. AI Governance, Vendor Ecosystem & Platform Standards – 15%

Establish and enforce a Responsible AI framework for all automation and AI initiatives—covering bias detection, fairness testing, explainability requirements, and ethical use guidelines calibrated to insurance regulatory requirements.

Ensure all AI agent decisions that impact client operations are explainable, auditable, and compliant with applicable insurance regulations—building durable trust with clients, auditors, and regulators.

Define and maintain AI governance standards including human oversight protocols, model documentation requirements, production monitoring thresholds, and incident response procedures for AI system failures.

Build and manage the strategic vendor ecosystem: evaluate AI/ML technology partners, negotiate licensing and co-development agreements, and maintain a curated landscape of orchestration platforms, LLM providers, intelligent document processing tools, and adjacent automation technologies.

Partner with Security and Compliance to ensure all AI deployments meet ReSource Pro’s ISMS policies, data privacy standards, and applicable insurance regulatory requirements across operating jurisdictions.

Stay ahead of evolving AI regulations (EU AI Act, US executive orders, India DPDP Act, China AI governance) and proactively adapt ReSource Pro’s AI practices to maintain compliance and competitive positioning.

5. People Leadership, Org Development & IP – 15%

Build and lead a high-caliber innovation organization—recruiting, developing, and retaining top AI/ML engineering, solution architecture, and applied research talent across India.

Create a culture of disciplined experimentation: one that tolerates calculated failure, rewards intellectual honesty about what works, and moves quickly from insight to production-grade deployment.

Define clear career architecture for the India innovation team—including progression paths for AI/ML Engineers, Automation Architects, Solution Designers, and Applied AI Researchers—and actively develop direct reports toward senior and director-level roles.

Develop deep technical expertise within the team through structured learning programs in AI/ML, cloud-native architectures, orchestration platforms, MLOps, prompt engineering, and responsible AI.

Conduct rigorous performance management, 1:1 coaching, and career development conversations—building a team that can operate with increasing autonomy as the program scales.

Establish and maintain an innovation knowledge base capturing solution patterns, architectural decisions, POC outcomes, and lessons learned—preventing duplication and enabling the broader organization to build on prior work.

Identify patentable innovations in AI-driven automation and work with legal counsel to protect ReSource Pro’s intellectual property portfolio as a long-term competitive asset.

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