Director - Automation Program

Director – AI & Automation | India

Detailed Job Description

1. Job Summary

• The Director will lead the AI and automation innovation agenda for ReSource Pro?s India center, serving as the primary

leader responsible for discovering, designing, scaling, and deploying AI-native solutions.

• Own the complete innovation lifecycle: technology scouting, AI/automation strategy, solution architecture, rapid

prototyping, POC and pilot execution, production deployment, and continuous improvement.

• Lead AI/ML Engineers, Automation Architects, Solution Designers, and Applied AI talent, while serving as a technical

thought leader for executives, clients, and the broader industry.

2. Innovation Strategy & AI Architecture

• Define the multi-year AI and automation innovation roadmap.

• Identify technology opportunities in Agentic AI, LLM orchestration, Intelligent Document Processing, multi-agent

systems, and AI-native workflow design.

• Define enterprise-level AI solution architectures, reference architectures, integration patterns, and platform standards.

• Design multi-agent architectures, RAG pipelines, tool-augmented workflows, and human-AI collaboration models.

• Evaluate whether processes should be redesigned or eliminated before automation.

• Maintain a technology radar covering foundation models, agentic frameworks, MLOps, and synthetic data.

• Conduct build-vs-buy-vs-partner evaluations and represent the organization at industry and client innovation forums.

3. Solution Design, Prototyping & Delivery

• Lead high-value AI automation initiatives across policy administration, claims processing, underwriting, billing, and

document intake.

• Define innovation hypotheses, develop MVPs/prototypes, execute controlled pilots, establish success criteria, and

validate production readiness.

• Drive a fast prototype-to-production pipeline without compromising technical quality or operational stability.

• Ensure technical specifications, integration requirements, confidence tiers, and operating procedures are documented.

• Lead client discovery and innovation workshops and identify high-ROI automation opportunities.

• Partner with Data Strategy/Data Services on data pipelines, labeling frameworks, evaluation datasets, and feature

stores.

4. Technical Excellence & FDE Enablement

• Build configurable, documented, robust, scalable solutions suitable for a Forward Deployed Engineer (FDE) operating

model.

• Combine Agentic AI, Intelligent Document Processing, LLM orchestration, and confidence-tiered quality frameworks.

• Define standards for modularity, observability, failure handling, version control, rollback, and production monitoring.

• Establish velocity metrics including time-to-prototype, time-to-pilot, time-to-production, and reuse rate.

• Serve as the primary technical escalation point for architectural changes, model updates, and root-cause

investigations.

5. AI Governance & Responsible AI

• Establish a Responsible AI framework covering bias detection, fairness testing, explainability, and ethical AI usage.

• Ensure AI decisions affecting client operations are explainable, auditable, and compliant.

• Define human oversight protocols, model documentation, monitoring thresholds, and AI incident response procedures.? Partner with Security and Compliance on ISMS, privacy, and insurance regulatory requirements.

• Track evolving requirements including the EU AI Act, US AI-related executive orders, India DPDP Act, and China AI

governance.

6. Vendor & Technology Ecosystem

• Evaluate AI/ML technology partners, LLM providers, AI orchestration platforms, Intelligent Document Processing tools,

and adjacent automation technologies.

• Manage licensing and co-development discussions and maintain a curated AI technology landscape.

7. People Leadership & Organization Development

• Recruit, develop, and retain AI/ML engineering, solution architecture, and applied research talent.

• Build a culture of disciplined experimentation and production-grade delivery.

• Create career paths for AI/ML Engineers, Automation Architects, Solution Designers, and Applied AI Researchers.

• Provide coaching, performance management, career development, and structured learning in AI/ML, cloud

architecture, orchestration, MLOps, prompt engineering, and Responsible AI.

• Maintain an innovation knowledge base and identify patentable AI-driven innovations.

8. Minimum Qualifications

• Education: Master?s in AI/ML, Computer Science, or related technical field preferred; Bachelor?s with substantial

equivalent industry experience accepted; PhD welcomed for strong research and production AI backgrounds.

• Certifications strongly preferred: AWS Solutions Architect / ML Specialty, Azure AI Engineer / Solutions Architect, or

GCP Professional ML Engineer.

• Responsible AI / AI Ethics certifications are highly valued; PMP/CPM and formal training in LLM fine-tuning,

orchestration frameworks, or MLOps are valued.

9. Experience Requirements

• 18+ years total experience in technology, automation, AI/ML, or software engineering.

• 8+ years in innovation leadership, R&D;, or solution architecture.

• 5+ years building and deploying production AI/ML systems including LLMs, agentic workflows, NLP, computer vision,

or Intelligent Document Processing.

• 5+ years leading technical teams including managers and senior architects.

• 3+ years in data strategy, data engineering, or MLOps.

• Demonstrated track record of scaling AI solutions from prototype to production in a commercial services environment.

• Insurance, BPO, financial services, or professional services experience is strongly preferred.

10. Mandatory Technical Skills

• AI/ML, LLM, Generative AI, Agentic AI, multi-agent systems, LLM orchestration, RAG, NLP, and Intelligent Document

Processing.

• Frameworks: LangChain, Semantic Kernel, AutoGen, CrewAI, or equivalent.

• Architecture: enterprise AI architecture, cloud architecture, AI-native platforms, data pipelines, human-in-the-loop and

integration architecture.

• Programming/ecosystem: Python, Hugging Face, TensorFlow and/or PyTorch.

• Cloud AI: Azure OpenAI, AWS Bedrock, and GCP Vertex AI.

11. MLOps & Data Engineering

• Preferred: Airflow, dbt, Spark, feature stores, model registries, MLOps, and data engineering practices supporting the

full AI production lifecycle.12. Domain Experience

• Insurance operations: policy administration, claims, underwriting, billing, and document intake.

• Other preferred industries: BPO, financial services, and professional services.

13. Leadership & Business Skills

• Innovation leadership, executive presence, strategic thinking, solution architecture, client engagement, business-case

development, stakeholder management, cross-functional leadership, cross-geography team management, technical

mentoring, and ambiguity management.

• Ability to translate complex AI architectures into clear business cases for C-suite audiences while maintaining technical

credibility with senior engineers.

14. Preferred Additional Skills

• Insurance operations expertise; published thought leadership; conference presentations; research papers; patents;

AI/automation blogs.

• Platform-based or services-as-software architecture; reusable AI components and templates.

• Familiarity with UiPath, Power Automate, or Automation Anywhere.

15. Travel

• 15–20% international travel, including collaboration with China delivery teams, US executive engagement, client

innovation workshops, and industry conferences.Top 10 Screening Parameters

# Screening Parameter What to Look For

1 AI/GenAI Architecture LLM, Agentic AI, RAG, multi-agent architecture

2 AI Production Experience Prototype – Pilot – Production

3 AI Innovation Leadership 8+ years innovation/R&D/architecture leadership

4 Overall Experience 18+ years technology/AI/automation

5 LLM & Agentic Frameworks LangChain, AutoGen, CrewAI, Semantic Kernel or equivalent

6 Solution Architecture Enterprise-scale AI platforms, cloud, data and integration

7 Technical Leadership Leading AI/ML engineers, architects and managers

8 Python & AI Stack Python, Hugging Face, TensorFlow/PyTorch, Azure OpenAI/AWS Bedrock/GCP Vertex

9 AI Governance Responsible AI, explainability, compliance, human oversight

10 Insurance/Domain Experience Claims, underwriting, policy, billing, document processing

Core profile: 18+ years technology leader with 5+ years hands-on production AI/ML and strong expertise in LLMs,

Agentic AI, RAG, multi-agent orchestration, enterprise AI architecture, innovation leadership, and scaling AI solutions

from POC to production.