Enterprise Architect

Job Description

The Enterprise Architect will be responsible for designing, governing, and evolving large‑scale enterprise solutions leveraging Java‑based platforms, Generative AI capabilities, and Big Data ecosystems. This role requires close collaboration with business stakeholders, delivery teams, and leadership to ensure scalable, secure, and future‑ready architectures aligned with business strategy.

Technical Skills & Expertise

• Java/J2EE, Spring Boot, REST APIs, Microservices architecture
• Cloud Platforms: GCP (architecture & deployment)
• DevOps & Platforms: Docker, Kubernetes, CI/CD pipelines

• Hands‑on experience with LLMs (OpenAI, Claude, Llama, Mistral, etc.)
• RAG architectures, Vector Databases (Pinecone, FAISS, OpenSearch, etc.)
• Frameworks & tools: LangChain, LangGraph, Agentic AI frameworks
• Prompt engineering, orchestration, and AI governance practices

• Data processing frameworks: PySpark, Scala, Hive, Kafka, Databricks
• Data platforms: Data Lakes, Lakehouse architectures
• SQL & NoSQL databases
• Data governance, quality, and metadata management

• Enterprise integration patterns
• System scalability, performance, and resiliency design
• Security‑by‑design and compliance (enterprise standards)
• Experience working in large enterprise or regulated environments

GCP, Pyspark, Java, SQL, Architect

• Define and own enterprise architecture vision, principles, standards, and reference architectures
• Translate business goals and roadmaps into technology blueprints and transition architectures
• Evaluate trade‑offs across scalability, performance, cost, security, and time‑to‑market
• Govern architecture decisions across multiple teams and programs
• Ensure alignment of solutions with long‑term enterprise and product strategy
• Mentor architects and senior engineers; guide technical decision‑making across teams

• Architect large‑scale enterprise applications using Java/J2EE, Spring Boot, and microservices
• Define REST‑based, event‑driven, and API‑first integration patterns
• Guide modernization of legacy monoliths into cloud‑native and microservices‑based systems
• Ensure best practices for JVM performance, resilience, caching, and fault tolerance
• Set standards for code quality, design patterns, and reusable enterprise components

• Lead Generative AI use‑case discovery, design, and implementation across business functions
• Design enterprise‑grade RAG architectures, AI agents, assistants, and automation workflows
• Own GenAI architecture aspects such as:
• LLM selection and orchestration
• Prompt engineering strategies
• Vector database design and embedding pipelines
• AI workflow orchestration and agent frameworks
• Define governance for responsible AI, security, privacy, and cost controls
• Present and demonstrate AI solutions directly to client and executive stakeholders

• Design and govern enterprise data platforms including data lakes, lakehouse, and analytics layers
• Architect batch, near‑real‑time, and streaming data pipelines
• Enable AI‑ready data architectures with metadata management, lineage, and quality controls
• Guide teams on scalable storage, compute, and query patterns for high‑volume data systems
• Ensure tight integration between transactional systems and analytical platforms
• Nice to have experience in Mainframe migration using PySpark.

• Define hybrid and cloud‑native architectures on GCP
• Architect containerized platforms using Docker and Kubernetes
• Drive DevOps and CI/CD strategies for faster, reliable delivery
• Align infrastructure and platform decisions with application, AI, and data needs
• Optimize architecture for cost, resilience, and operational efficiency

• Ensure solutions meet enterprise security, compliance, and regulatory standards
• Embed security‑by‑design and privacy‑by‑design principles
• Review architecture for performance, scalability, availability, and disaster recovery
• Support audits, risk assessments, and architectural sign‑offs

• Act as a technical authority across multiple teams and programs
• Mentor architects, tech leads, and senior engineers
• Review and guide solution designs, POCs, and complex implementations
• Collaborate closely with product managers, PMO, delivery managers, and leadership
• Mentor architects and senior engineers; guide technical decision‑making across teams
• Support pre‑sales, RFP responses, SOW design, and effort estimations when required