Solution Architect

Location: Gurgaon or Hyderabad

Role: Solution Architect GenAI & Agentic AI Platform

About the Role:

We are looking for a highly skilled and visionary Solution Architect to lead the technical design and architectural direction of AI-powered enterprise platforms, with a specific focus on Generative AI technologies and Agentic AI frameworks. This role is central to translating complex business needs into scalable, secure, and high-performance AI solutions that can power next-generation applications across commercial and operational domains.

The ideal candidate should have a strong background in enterprise-grade solution architecture, experience with full-stack systems, and hands-on familiarity with GenAI tools, orchestration platforms, and multi-agent systems. The role will closely collaborate with engineering, product, and data science teams to drive innovation and ensure successful platform delivery.

Key Responsibilities

1. Architecture Assessment & Scalability Planning

• Assess current platform architecture, including data pipelines, model orchestration, APIs, and integrations.
• Identify bottlenecks and propose refactoring strategies to support rapid scale deployment of use cases
• Define a scalable, modular reference architecture adaptable across brands, therapy areas, and geographies.
• Develop blueprints for horizontal and vertical scaling (compute, storage, model-serving, agent orchestration).

2. Code Governance & Quality Assurance

• Act as the gatekeeper of the core codebase, reviewing backend, frontend, and ML/AI components.
• Establish and enforce coding standards (naming conventions, modularity, documentation, and security practices).
• Integrate automated code quality checks (linting, static analysis, vulnerability scanning) into CI/CD.
• Ensure proper design patterns for agent orchestration, RAG pipelines, and analytics workloads.

3. Environment Strategy: Dev, QA, and Prod

• Define and enforce environment strategies (Dev sandbox, QA integration/UAT, Production HA/Compliance).
• Implement CI/CD pipelines with approval gates, rollback strategies, and model/data version control.
• Ensure automated regression, unit, and performance testing in every release cycle.

4. Multi-Cloud Deployment Strategy

• Own the multi-cloud architecture (AWS, Azure, GCP) for flexibility, redundancy, and compliance.
• Build cloud-agnostic infrastructure-as-code (IaC) templates (Terraform, Pulumi) for repeatable deployments.
• Optimize cost, performance, and compliance trade-offs across providers.
• Ensure service portability and interoperability of AI models across clouds.

5. Compliance & Security by Design

• Enforce adherence to HIPAA, GDPR, FDA 21 CFR Part 11, GxP, and other life sciences regulations.
• Embed audit logging, explainability, and traceability into all AI/ML workflows.
• Implement RBAC, encryption (in-transit & at-rest), and secure key management.
• Build MLR compliance workflows into AI-driven content/insight pipelines.

6. Technical Team Leadership

• Provide hands-on technical direction to backend, frontend, data engineering, and DevOps/MLOps teams.
• Mentor engineers and align all technical workstreams to the central architecture blueprint.
• Conduct design reviews, sprint reviews, and retrospectives to uphold architectural integrity.

7. GenAI Technology Radar & Innovation

• Track global trends in Generative AI, multi-agent frameworks, LLM orchestration, and healthcare analytics.
• Evaluate and recommend emerging technologies (vector DBs, multimodal models, healthcare-specific LLMs).
• Champion incremental platform improvements aligned with ciATHENA's strategic vision.

Required Qualifications:

• Minimum 8 years of experience, with 3+ years in enterprise solution architecture with more recent exposure architecting Gen AI and Agentic Ai solutions.
• Proven experience architecting and delivering large-scale platforms, preferably in SaaS or AI/ML-driven environments.
• Strong hands-on experience with Generative AI technologies (e.g., OpenAI, Hugging Face, Anthropic, LLaMA, Claude).
• Deep understanding of Agentic AI frameworks, multi-agent orchestration, vector databases, and retrieval-augmented generation (RAG).
• Proficiency in building and integrating microservices, APIs, and data pipelines in modern cloud environments (AWS, Azure, GCP).
• Familiarity with front-end and back-end technologies including React, Node.js, Python, FastAPI, etc.
• Strong grasp of AI model lifecycle, including prompt tuning, fine-tuning, caching strategies, and observability.

Preferred Qualifications:

• Exposure to Life Sciences or Healthcare domain is a plus.
• Experience with MLOps, model monitoring, or production deployment of AI models.
• Prior involvement in building or deploying chatbot frameworks, intelligent agents, or autonomous task execution platforms.
• Knowledge of responsible AI, bias detection, and compliance considerations for enterprise AI platforms.