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Salary
$44k – $95k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Architect · 15+ years exp
Overview
Company
Impact
Profile match
Akrys AI is a healthtech company building a clinical intelligence platform to convert doctor-patient conversations into structured records and clinical notes, offering AI-assisted support for healthcare workflows.

The Vice President - Engineering and Architecture will serve as the senior-most technical leader below the CTO, responsible for designing, governing, and evolving the architecture of the Akrys platform. This role combines deep technical expertise with engineering leadership. You will define the architecture of our AI, application, platform, and cloud systems while working closely with Engineering Team Leads to ensure consistent, scalable, secure, and high-quality execution.

You will be expected to remain hands-on in architecture, actively participate in technical design discussions, review critical implementations, mentor engineering teams, and drive engineering excellence across the organisation.

The candidate will have responsibilities across the following functions:

Architecture Leadership:

  • Own the end-to-end architecture of the Akrys platform.
  • Design scalable, modular, secure, and cloud-native systems capable of supporting enterprise healthcare organisations.
  • Define architecture standards, engineering principles, design patterns, and governance frameworks.
  • Review and approve architecture for all major product initiatives.
  • Drive architectural consistency across frontend, backend, AI, infrastructure, and data platforms.
  • Ensure systems are maintainable, extensible, and production-ready.

AI Platform Architecture:

  • Lead the architectural design of AI-powered healthcare systems, including: Large Language Models (LLMs)
  • Agentic AI workflows
  • Multi-agent orchestration
  • Retrieval-Augmented Generation (RAG)
  • Clinical reasoning pipelines
  • Prompt orchestration
  • Semantic search
  • Embeddings
  • AI evaluation frameworks
  • LLMOps
  • AI observability
  • Model deployment and inference optimisation
  • Ensure AI systems are scalable, reliable, explainable, and suitable for real-time clinical workflows.

Product Engineering:

  • Provide technical leadership across: Clinic Management System, Hospital Information System, Electronic Medical Records (EMR/EHR), OPD & IPD workflows, Laboratory Management, Radiology, Pharmacy, Inventory, Billing, ICU & Critical Care, Operation Theatre Management, Analytics, Mobile applications, Third-party integrations, Enterprise APIs.

Engineering Leadership:

  • Lead and mentor Engineering Team Leads and senior engineers.
  • Partner with Product, Design, QA, Clinical, and AI teams to translate product requirements into scalable technical solutions.
  • Drive engineering best practices, coding standards, and technical quality.
  • Conduct architecture reviews, design reviews, and code reviews for critical components.
  • Improve engineering productivity through reusable frameworks and platform capabilities.
  • Guide technical decision-making across engineering teams.

Cloud and Platform Engineering:

  • Architect and optimise: AWS cloud infrastructure, Kubernetes, Docker, Infrastructure as Code (Terraform), CI/CD pipelines- high availability, Disaster Recovery, Distributed systems, Event-driven architectures, Observability, performance optimisation, Infrastructure cost optimisation.

Data and Platform Architecture:

  • Design scalable data platforms supporting: PostgreSQL, MongoDB, Redis, Elasticsearch/OpenSearch, Vector databases, Data modeling, Event-driven architecture, Analytics platforms, Clinical data pipelines, Knowledge management systems.

Technical Governance:

  • Establish architecture review processes.
  • Define engineering standards and documentation practices.
  • Drive technical debt reduction initiatives.
  • Evaluate emerging technologies and recommend adoption where appropriate.
  • Ensure platform security, scalability, reliability, and compliance.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field.
  • 15+ years of experience in software engineering and architecture.
  • 5+ years leading engineering teams or architecture for enterprise SaaS products.
  • Proven experience building and scaling cloud-native enterprise platforms.
  • Strong experience designing distributed systems and microservices.
  • Demonstrated experience building AI-powered products from concept to production.

Technical Expertise:

  • AI & Generative AI, Large Language Models (LLMs), Agentic AI, Multi-Agent Systems, Retrieval-Augmented Generation (RAG, Prompt Engineering, Fine-Tuning, Embeddings, Semantic Search, AI Evaluation Frameworks, OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, DeepSeek, LangChain, LangGraph, LlamaIndex, Hugging Face, vLLM, TensorRT-LLM, Backend, Python, FastAPI, REST APIs, GraphQL, Microservices, Event-driven Architecture.
  • Authentication & Authorisation: Distributed Systems, Frontend, React, Next.js, TypeScript, Databases, PostgreSQL, MongoDB, Redis, Elasticsearch/OpenSearch, Pinecone, Weaviate, Milvus, ChromaDB.
  • Cloud & Infrastructure: AWS, Kubernetes, Docker, Terraform, CI/CD, GitHub Actions, ArgoCD, Monitoring & Observability.

Leadership Expectations:

  • The ideal candidate will: Take complete ownership of the platform architecture.
  • Balance long-term architectural vision with pragmatic execution.
  • Build strong engineering practices without introducing unnecessary complexity.
  • Mentor engineering teams through technical leadership.
  • Foster a culture of ownership, quality, innovation, and continuous improvement.
  • Work closely with the CTO to execute the company's technology roadmap.

Success Metrics:

Success in this role will be measured by:

  • Delivery of scalable and resilient platform architecture.
  • Successful implementation of AI capabilities across the platform.
  • Engineering velocity while maintaining high code quality.
  • Platform reliability, security, and performance.
  • Reduced technical debt.
  • High engineering productivity.
  • Efficient cloud infrastructure utilisation and cost optimisation.
  • Successful delivery of strategic product initiatives.
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