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 DevOps 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 NoSQL).
• Experience with API design and management.
• Familiarity with DevOps 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., PostgreSQL, MySQL), NoSQL (e.g., MongoDB, Cassandra).
• Web Technologies: [Specify relevant frontend/backend frameworks and technologies, e.g., React, Angular, Vue.js, Spring Boot, Node.js].
• DevOps & MLOps Tools: Git, Jenkins, GitHub Actions, GitLab CI, Docker, Kubernetes, [relevant ML lifecycle tools].
• API Technologies: REST, GraphQL.
• Other: Messaging queues (e.g., Kafka, RabbitMQ), Caching mechanisms, Monitoring and logging tools.