GEN AI Architect

Roles and Responsibilities :

• Design and develop large language models using Python, focusing on agentic AI capabilities.
• Collaborate with cross-functional teams to integrate generative AI solutions into existing systems.
• Develop and maintain complex neural networks for natural language processing tasks such as text classification, sentiment analysis, and machine translation.
• Conduct research on cutting-edge techniques in deep learning and neural networks to stay up-to-date with industry trends.

Job Requirements :

1. Strong experience in enterprise solution architecture and application modernization.
2. Hands-on understanding of LLMs, RAG, embeddings/vector databases, tool/function calling, agent orchestration, multi-agent workflows, MCP, prompt/context engineering, AI evaluation, guardrails, and human-in-the-loop patterns
3. Strong hands-on experience with AWS/ Google/ Azure cloud architecture and services.
4. Experience with modern frontend technologies such as React, Next.js, Angular, JavaScript, and TypeScript.
5. Strong backend experience with one or more of .NET/C#, Python, Node.js, Java, with the ability to understand and review solutions across different technology stacks.
6. Strong knowledge of REST APIs, OpenAPI/Swagger, API-first architecture, and enterprise integration patterns.
7. Experience with OAuth 2.0, OIDC, JWT, SSO, API security, IAM, and secrets management.
8. Experience with Docker, Kubernetes, ECS/EKS, serverless/Lambda, and cloud-native architectures.
9. Experience with relational databases such as PostgreSQL, SQL Server, AWS RDS/Aurora, including data integration and migration patterns.
10. Hands-on experience with Terraform, Pulumi, CloudFormation, or equivalent Infrastructure as Code technologies.
11. Strong understanding of CI/CD pipelines, GitHub Actions/Azure DevOps, branching strategies, and automated quality gates.
12. Experience with observability and monitoring platforms such as Datadog and AWS CloudWatch.
13. Understanding of microservices, modular architectures, event-driven architecture, distributed systems, and enterprise integration patterns.
14. Working knowledge of GenAI/LLMs, RAG, vector databases, AI agents, prompt engineering, and AI-enabled application development.
15. Strong experience using AI coding assistants for code generation, repository analysis, modernization, test generation, debugging, and documentation.