Job Summary
Lead ReSource Pro's AI and automation innovation agenda for the India Center by defining, designing, and scaling AI-native solutions that deliver measurable business outcomes and competitive advantage. Own the end-to-end innovation lifecycle—from technology scouting and solution architecture to rapid prototyping, pilot execution, and production deployment—building enterprise-grade solutions that support a Forward Deployed Engineer (FDE) operating model beyond traditional RPA. Lead a high-performing team of AI/ML engineers, automation architects, and solution designers while driving the technical vision for agentic AI, LLMs, intelligent document processing, and AI orchestration. Act as a trusted advisor to executive leadership, clients, and industry forums.
Principal Responsibilities
Innovation Strategy & AI Architecture
- Define and execute the enterprise AI and automation roadmap, identifying strategic investments in agentic AI, LLMs, RAG, intelligent document processing, multi-agent systems, and AI-native workflows.
- Own enterprise AI solution architecture, establishing reference architectures, integration patterns, platform standards, and scalable cloud, data, and orchestration frameworks.
- Design next-generation AI orchestration solutions using multi-agent systems, tool-augmented workflows, hybrid human-AI models, and first-principles thinking to transform business processes beyond traditional automation.
- Continuously evaluate emerging AI technologies, MLOps capabilities, and industry trends; provide executive recommendations and lead build-vs-buy-vs-partner decisions.
- Represent ReSource Pro as an AI thought leader with clients, executive stakeholders, industry events, and innovation forums.
Solution Design & Innovation Delivery
- Lead the design and delivery of AI-native automation solutions across insurance operations, improving productivity, accuracy, and operational efficiency.
- Drive a structured innovation lifecycle from ideation and rapid prototyping through pilot validation, production deployment, and continuous improvement.
- Establish delivery standards for technical quality, documentation, scalability, confidence frameworks, and operational readiness to enable repeatable deployments.
- Partner with business, data, and engineering teams to ensure production-ready data pipelines, evaluation frameworks, and measurable business outcomes.
- Conduct client workshops to identify high-value automation opportunities and support solution development and business growth.
Technical Excellence & Platform Leadership
- Build enterprise-grade AI platforms supporting configurable, scalable, and maintainable FDE operating models.
- Define engineering standards covering modular architecture, observability, resilience, version control, monitoring, rollback, and production support.
- Establish delivery KPIs including prototype velocity, production readiness, reuse, and deployment success while driving continuous improvement.
- Design trusted human-AI operating models with confidence scoring, governance, and human oversight.
- Provide architectural leadership for production issues, platform evolution, and technical enablement across engineering, delivery, and business teams.
AI Governance & Technology Ecosystem
- Establish Responsible AI governance covering explainability, fairness, bias mitigation, auditability, security, regulatory compliance, and human oversight.
- Develop enterprise AI governance standards for model lifecycle management, monitoring, documentation, and incident response.
- Manage strategic AI technology partnerships and evaluate platforms, LLM providers, orchestration frameworks, and automation technologies.
- Partner with Security and Compliance to ensure adherence to ISMS, privacy regulations, and global AI governance requirements while monitoring evolving AI regulations.
People Leadership & Innovation Culture
- Build, lead, and develop a world-class AI innovation organization across AI/ML Engineering, Solution Architecture, Automation, and Applied Research.
- Foster a high-performance culture of experimentation, innovation, continuous learning, collaboration, and accountable execution.
- Lead hiring, coaching, performance management, succession planning, and career development for technical leaders and engineers.
- Build organizational knowledge through reusable solution patterns, architectural standards, and innovation repositories while driving intellectual property development and patent opportunities.
Minimum Qualifications
- Degree - Bachelors / Masters
- Working Experience - 12–15 years of experience in technology, AI/ML, automation, or software engineering, including 8+ years in innovation leadership, 5+ years building production AI/ML solutions and leading technical teams, and 3+ years in data strategy/MLOps.
Behavioral/Technical Skills; Competencies
Required:
- Deep, hands-on technical expertise in AI/ML: demonstrated experience designing and deploying LLM-based systems, agentic AI workflows, multi-agent orchestration (LangChain, Semantic Kernel, AutoGen, CrewAI or equivalent), and RAG architectures in production environments
- Solution architecture capability at scale: proven ability to design end-to-end AI-native platforms integrating cloud services, data pipelines, orchestration layers, and human-in-the-loop components that can be deployed across multiple client engagements
- Demonstrated track record of taking AI solutions from proof-of-concept through pilot to full production deployment—with measurable business outcomes (FTE savings, cycle time reduction, accuracy improvement) attached to each
- Strong innovation leadership in ambiguous, fast-moving environments: building teams that experiment quickly, learn from failure, and ship production-grade solutions on aggressive timelines
- Cross-functional communication and executive presence: ability to translate complex AI/ML architectures into clear business cases for C-suite audiences, and to hold technical credibility with senior engineers simultaneously
- Experience managing cross-geography technical teams and driving structured innovation-to-delivery handoffs between R&D and production engineering organizations
- Strong understanding of responsible AI principles, AI governance frameworks, and the regulatory landscape for AI in insurance and financial services
- Proficiency in Python and familiarity with the broader AI/ML ecosystem (Hugging Face, TensorFlow/PyTorch, cloud AI services: Azure OpenAI, AWS Bedrock, GCP Vertex AI)
Preferred:
- Experience with MLOps tooling and data engineering practices (Airflow, dbt, Spark, feature stores, model registries) to govern the full AI production lifecycle
- Domain expertise in insurance operations (policy administration, claims, underwriting, document intake) to ground AI solution design in real workflow complexity
- Published thought leadership (conference talks, papers, patents, blogs) demonstrating standing in the AI/automation community
- Experience with platform-based or services-as-software architecture approaches—building reusable AI components and templates that scale across multiple client engagements
- Familiarity with automation platforms (UiPath, Power Automate, Automation Anywhere) as the foundation layer beneath more advanced AI orchestration
