PeopleLogic
Website:
peoplelogic.in
Job details:
OB DESCRIPTION
Role: Enterprise AI architect
Experience: 15-23 years
Mandatory skills -AIML, GEN AI, Enterprises Architect, Agentic AI
Purpose of this Role
- The Lead AI Architect (AI) is responsible for defining, adopting, and assuring AI-enabled data architectures that are commercially viable, technically robust, and aligned to enterprise and data strategy.
- As part of the International Health Global Architecture function, this role sets architectural direction and ensures strategic priorities are realized through data and AI delivery.
- This role will drive innovation, productivity and operational improvements, and customer experience transformation by applying modern AI architecture in a secure, scalable, and responsible manner.
- This role collaborates closely with business leaders, product teams, engineering, and third-party partners to ensure data and AI investments deliver measurable value while meeting regulatory, operational, and security requirements.
Main Duties / Responsibilities
Strategy & Architecture Leadership
- Architect and own enterprise-scale Generative AI platforms supporting multiple business use cases, including internal pilots, automation assistants, knowledge discovery, and customer-facing AI experiences.
- Define and maintain reference target-state data and AI architectures for International Markets, aligned to enterprise architecture standards and data strategy.
- Act as a senior architectural authority for AI-enabled data solutions, ensuring consistency, reuse, and long-term sustainability across the landscape.
- Advocate for AI-driven data design, embedding modern AI and analytics patterns into enterprise data platforms and services.
- Maintain an active awareness of emerging trends in data architecture, AI/ML, and enterprise technology, assessing their potential impact and value for advising senior leadership on AI strategy, feasibility and adoption paths.
AI Architecture
- Define architectural patterns and standards to support AI model lifecycle management, AI and data pipelines, feature engineering, and associated design artefacts.
- Architect and own enterprise-scale Generative AI platforms supporting multiple business use cases, including internal pilots, automation assistants, knowledge discovery, and customer-facing AI experiences.
- Define and maintain reference architectures and design patterns for LLM-based applications, covering model access, orchestration, retrieval, prompt management, evaluation, and observability.
- Ensure that AI architectures support the needs of data science, analytics, and AI use cases, including scalability, performance, data quality, and governance.
- Work with Solution Architects and engineering teams to translate business and technical requirements into coherent end-to-end data and AI solution designs.
Delivery & Governance
- Participate across the full delivery lifecycle, from early concept shaping and investment cases through to design assurance and governance during build and implementation.
- Provide architecture governance through design reviews, standards definition, and participation in architecture review and approval forums.
- Capture and manage architectural risks, issues, and assumptions, articulating their financial, operational, and delivery impacts.
- Support sponsors in the creation of well-rounded and compelling business cases for data and AI-led change.
Stakeholder & Commercial Leadership
- Proactively engage with stakeholders across Business, IT, and third-party partners to ensure solutions are cost-effective, appropriate, and aligned to business outcomes.
- Take a lead role in the selection and assessment of third-party data and AI solutions, establishing and maintaining effective supplier and partner relationships where required.
- Apply strong commercial awareness, including financial planning, budgeting, and cost-benefit considerations, in architectural decision-making.
Capability Building & Mentoring
- Provide high-level mentoring and guidance to architects, design, and development teams to embed data and AI principles, standards, and best practices.
- Take a lead role in the selection and assessment of third-party data and AI solutions, establishing and maintaining effective supplier and partner relationships where required.
- Contribute to the maturity of data and AI architecture capability through knowledge sharing, standards development, and continuous improvement.
- Apply strong commercial awareness, including financial planning, budgeting, and cost-benefit considerations, in architectural decision-making.
Skills & Experience
Essential Experience
- Demonstrated expertise in architecting and leading large-scale AI initiatives, taking solutions from proof of concept through to successful production deployment.
- Deep understanding of architectural considerations and trade-offs in AI and machine learning projects, built through hands-on delivery experience.
- Experience working effectively within globally distributed teams and complex stakeholder environments.
Generative AI & Core Concepts:
- 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 on foundational models and inference profiles, regional hosting and restrictions of various models and be able to identify the right model for the right requirements.
AI Architecture & Tooling
- 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 in 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 Tex tract, 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 Lang Chain, 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.
- Familiarity with AI governance, model lifecycle considerations, and responsible use of data and AI, particularly within regulated environments.
Qualifications
- 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, with English at C2 level.
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