Building a GenAI Engineering Team for a Health Insurance GCC
Accelerating Generative AI adoption in a highly regulated industry demands specialized talent, strategic execution, and deep domain understanding. When a leading health insurance enterprise set out to establish an AI/ML engineering capability within its Global Capability Center (GCC), PeopleLogic partnered to make it happen. Through precise role alignment, data-driven talent intelligence, and compelling storytelling, we built a high-performing GenAI team that enabled enterprise-scale innovation in record time.
Premise: The client, a top-tier health insurance company, aimed to fast-track its Generative AI roadmap by creating an in-house AI/ML engineering team within its Bengaluru GCC. The goal was to unlock value from vast amounts of unstructured insurance data—claims, policies, and customer interactions—through secure, production-ready AI solutions.
However, the challenge was significant: finding engineers skilled in Python, Transformers, LangChain, RAG, and prompt engineering—when the GenAI talent market was just emerging.
Challenge: The project required filling 15 specialized AI/ML roles—including 5 Lead ML Engineers and 10 Senior ML Engineers—under stringent timelines. Key hurdles included:
Limited availability of AI/ML professionals with hands-on GenAI experience.
Strict compliance with healthcare data privacy and regulatory standards.
High benchmarks for model accuracy, explainability, and uptime.
Attract niche engineers in a competitive market — requiring a compelling value proposition beyond compensation.
Need for rapid delivery without compromising enterprise-grade quality.
To assess problem-solving and critical thinking, shortlisted candidates were given a complex case study at the end of the final round, testing real-world technical decision-making.
PeopleLogic Approach and Strategy
PeopleLogic Approach
Clear Role Definition & Alignment
We collaborated with client leadership to define role expectations that prioritized production-grade GenAI development over experimental projects—ensuring clarity in both capability and accountability.
Market Mapping & Deep Talent Market Intelligence
Through detailed market mapping and compensation benchmarking, we identified engineers proficient in:
Python-based ML pipelines
Transformer models (BERT)
LangChain-driven RAG architectures
Prompt engineering and LLM orchestration
Creative & Targeted Sourcing
Our sourcing extended beyond Bengaluru to tap into niche GenAI talent nationwide. We engaged passive candidates active in open-source AI, NLP research, and GenAI communities, going far beyond conventional recruitment channels.
Compelling Candidate Storytelling
We positioned the opportunity as a career-defining project — to build and deploy GenAI systems that directly impact millions of insurance customers. This narrative resonated strongly with engineers seeking meaningful, applied AI work.
Continuous Collaboration
Through ongoing syncs with hiring managers and candidates, we maintained tight feedback loops, ensuring precision, speed, and high offer acceptance.
Outcome
15 AI/ML experts hired within 45–60 days.
Established a scalable GenAI capability within the GCC.
Enabled deployment of real-world AI use cases, including:
Claims document summarization
Policy and coverage Q&A
Intelligent customer support assistants
Impact
This collaboration accelerated the client’s Generative AI adoption, improving efficiency, accuracy, and compliance across insurance operations. It positioned the GCC as a center of excellence for AI and GenAI, ready to scale innovation across global workflows.
Result
A future-ready AI/ML engineering team—built with precision, speed, and strategy—enabling the client to deliver secure, production-grade Generative AI solutions at enterprise scale.
