AI Enterprise Architect

Location
- Hyderabad, Chennai, Bangalore, and Pune (preferred in that order)

Key Responsibilities:

- Design and architect end-to-end solutions for complex business problems, considering scalability, performance, security, and cost-effectiveness.
- Lead the design and implementation of microservices-based architectures, defining service boundaries, APIs, and interaction patterns.
- Architect and integrate Generative AI and Machine Learning models into applications, defining data pipelines, model deployment strategies, and inference mechanisms.
- Collaborate with data scientists, ML engineers, and development teams to translate models into production-ready, scalable solutions.
- Define and enforce architectural standards, patterns, and best practices across application development teams.
- Evaluate and select appropriate technologies, frameworks, and tools for application development, microservices, AI/ML integration, and cloud deployment.
- Design and optimize solutions for deployment on at least one major cloud platform (AWS, Azure, or GCP), leveraging relevant cloud services (e.g., compute, storage, databases, AI/ML services, networking).
- Provide technical leadership and guidance to development teams throughout the software development lifecycle.
- Create and maintain technical documentation, including architectural diagrams, design specifications, and technical guidelines.
- Collaborate with stakeholders, including product managers, business analysts, and other architects, to understand requirements and translate them into technical designs.
- Stay abreast of the latest trends and advancements in microservices, GenAI, Machine Learning, cloud computing, and web technologies.
- Drive innovation by identifying opportunities to leverage new technologies and approaches to improve our applications and processes.
- Assess and mitigate technical risks.
- Support the implementation of Dev Ops and MLOps practices for seamless deployment and management of applications and models.
- Contribute to the development of the technical roadmap and strategy.

Required Qualifications:

- Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent practical experience.
- 12-17 years of experience in solution architecture, with a strong focus on application development.
- Proven experience in designing and implementing microservices architectures.
- Demonstrated experience with Generative AI concepts, models (e.g., LLMs), and their application in software solutions.
- Solid understanding of Machine Learning principles, workflows, and the integration of ML models into applications.
- Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.
- Proficiency in at least one modern programming language (e.g., Python, Java, Node.js, C#).

- Experience with web application development technologies and frameworks (frontend and/or backend).
- Strong understanding of database technologies (relational and/or No

SQL).

- Experience with API design and management.
- Familiarity with Dev Ops principles and CI/CD pipelines.
- Excellent communication, presentation, and interpersonal skills with the ability to explain complex technical concepts to both technical and non-technical audiences.
- Strong analytical and problem-solving skills.

Technical

Skills:

- Architectural Patterns:
Microservices, Event-Driven Architecture, API Gateway, etc.
- Cloud Platforms: AWS (EC2, S3, Lambda, Sagemaker, etc.), Azure (VMs, Blob Storage, Azure ML, etc.), GCP (Compute Engine, Cloud Storage, Vertex AI, etc.).
- Programming

Languages:

Python, Java, Node.js, C#, [any other relevant languages].
- AI/ML:
Generative AI models (LLMs, Diffusion Models), Machine Learning algorithms, model training and inference, MLOps.
- Databases: SQL (e.g., Postgre

SQL, MySQL), No

SQL (e.g., Mongo

DB, Cassandra).
- Web Technologies: [Specify relevant frontend/backend frameworks and technologies, e.g., React, Angular, Vue.js, Spring Boot, Node.js].
- Dev Ops & MLOps Tools:
Git, Jenkins, Git Hub Actions, Git Lab CI, Docker, Kubernetes, [relevant ML lifecycle tools].
- API Technologies: REST, Graph

QL.
- Other:
Messaging queues (e.g., Kafka, Rabbit

MQ), Caching mechanisms, Monitoring and logging tools.