AI Architect - Cigna Healthcare

Overview

Drive the architecture, design, and adoption of enterprise-grade Generative AI (GenAI) solutions across Cigna International Health, applying modern AI architectures in a secure, scalable, and responsible manner.

What you'll do

• Define and maintain blueprints, reference architectures and design patterns for LLM-based applications, covering model access, orchestration, retrieval, prompt management, evaluation, and observability.
• Translate business and product requirements into robust AI solution architectures that balance innovation, reliability, cost efficiency, and risk management.
• Act as a GenAI subject matter expert, advising senior leadership on AI strategy, feasibility, and adoption paths.
• Architect knowledge bases and RAG architectures integrating structured and unstructured enterprise data sources.
• Establish agentic AI patterns, enabling multi-step reasoning, tool usage, workflow orchestration, and human-in-the-loop controls.
• Guide teams on prompt engineering, context management, and response grounding to improve accuracy, relevance, and user trust.
• Assist in the evaluation of commercial LLMs and SaaS AI platforms, selecting the right tools for different workloads.
• Centralize access to LLMs and AI services through an AI Gateway / Model Access Layer, covering authentication and authorization, model routing and versioning, rate limiting, quotas, cost controls, logging, auditability, and observability.
• Establish secure connectivity to LLM endpoints in the enterprise cloud, documenting DNS, routing, IAM policies, firewall rules, and network controls.
• Integrate GenAI capabilities into existing enterprise systems and digital platforms.
• Recommend layered guardrails across GenAI systems.
• Embed Responsible AI principles (fairness, transparency, explainability, and accountability) into architecture and delivery practices.
• Partner closely with security, privacy, legal, and compliance teams to meet enterprise governance standards and emerging AI regulations.
• Define monitoring and evaluation frameworks to detect hallucinations, drift, misuse, and operational risks.
• Drive GenAI initiatives focused on developer productivity, operational efficiency, and enhanced customer experiences.
• Stay current with emerging AI trends, tools, and architectures, assessing their applicability to enterprise use cases.
• Mentor engineers and architects, establishing best practices, architectural standards, and reusable components.
• Contribute to internal communities of practice and AI capability building.

What you'll need

• Large Language Models (LLMs), embeddings, prompt engineering.
• Retrieval-Augmented Generation (RAG), knowledge bases and multimodal embeddings.
• Agentic AI workflows and tool calling.
• Model Context Protocol (MCP) concepts and context management.
• Knowledge of foundational models and inference profiles, regional hosting and restrictions of various models, and ability to identify the right model for the right requirements.
• Fine-tuning strategies, adapters, and inference optimization.
• Awareness of AI Gateway / model access layer design and access controls on the gateways.
• Proficiency in designing and implementing AI Gateway/model access layers to facilitate secure and scalable access to AI models.
• Experience architecting solutions with vector databases and semantic search for efficient retrieval of information from large volumes of unstructured data.
• Skilled in leveraging document intelligence platforms (e.g., Azure Form Recognizer, AWS Textract, Google Document AI) for document classification, data extraction, and entity recognition from varied formats including scanned documents, PDFs, and handwritten forms.
• Knowledge of Intelligent Document Processing (IDP) solutions for automated ingestion, parsing, and validation of unstructured documents, ensuring accurate routing of documents to appropriate business workflows.
• Familiarity with workflow orchestration frameworks (such as LangChain, LangGraph, or equivalent) to automate end-to-end document processing pipelines, including document routing, exception handling, and integration with downstream systems.
• Understanding of optical character recognition (OCR), natural language processing (NLP) techniques, and entity linking for extracting actionable insights from diverse data types.
• Awareness of data validation, quality checks, and document lifecycle management best practices to ensure compliance and reliability in document processing solutions.
• End-to-end AI pipelines: ingestion, retrieval, generation, evaluation and rollout.
• Prompt-injection mitigation and jailbreak prevention.
• Content safety, moderation, and policy enforcement.
• Human-in-the-loop architectures.
• AI observability, monitoring, and evaluation frameworks.
• Familiarity with MLOps / LLMOps practices.
• Awareness of various data, privacy and legal standards across different geographical regions and ensuring the solution stays compliant with them.
• Experience architecting solutions with cloud platforms (Azure, AWS, or GCP) and their AI services.
• Experience architecting end-to-end AI solutions using various cloud services integrated with AI tools and runtimes.
• Secure API design, IAM, networking, and CI/CD integration.
• Designing systems for scale, resilience, and global deployment.
• Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 10+ years of experience building and architecting large-scale software or AI systems in medium-to-large enterprises.
• 5+ years of experience with AI/ML, NLP, or Generative AI.
• Experience working in regulated or compliance-sensitive environments (e.g., healthcare, finance, insurance, global enterprises).
• Strong communication skills with the ability to influence technical and non-technical stakeholders.

Details

• Location: Bengaluru, India.
• Multicultural and hybrid working environment.