Description

Key Responsibilities

• Design end-to-end solution architectures for complex enterprise applications using Java, Spring Boot, microservices, APIs, and event-driven architecture.
• Define cloud-native architectures using AWS, Azure, or Google Cloud Platform.
• Lead application modernization initiatives, including legacy transformation, containerization, and migration to cloud platforms.
• Design highly available, scalable, secure, and fault-tolerant systems.
• Establish architecture standards, reusable frameworks, design patterns, and engineering best practices.
• Develop architecture diagrams, solution blueprints, integration patterns, data flows, and technical design documents.
• Evaluate and recommend appropriate cloud services, technology platforms, frameworks, and development tools.
• Design and integrate AI-powered capabilities, including generative AI, AI agents, large language models, prompt engineering, and retrieval-augmented generation.
• Architect integrations with enterprise data sources, vector databases, APIs, and machine learning platforms.
• Evaluate AI models based on accuracy, security, scalability, latency, cost, and business requirements.
• Ensure AI solutions comply with responsible AI, data privacy, security, governance, and regulatory standards.
• Collaborate with business stakeholders, product owners, enterprise architects, developers, data engineers, DevOps teams, and cybersecurity teams.
• Provide technical leadership to development teams and conduct architecture, code, security, and performance reviews.
• Identify technical risks, dependencies, architectural gaps, and mitigation strategies.
• Define CI/CD, observability, logging, monitoring, and production support requirements.
• Support technical estimations, proof-of-concept development, vendor evaluations, and technology selection.
• Mentor engineers and promote engineering excellence across delivery teams.

Required Qualifications:

• 10+ years of experience in software engineering, application architecture, or solution architecture.
• 5+ years of experience designing enterprise-scale solutions.
• Strong hands-on experience with Java, Spring Boot, Spring Cloud, REST APIs, and microservices.
• Experience with distributed systems, event-driven architecture, Kafka, messaging platforms, and API gateways.
• Strong experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
• Experience with cloud services involving compute, storage, networking, databases, serverless technologies, security, and monitoring.
• Experience with Docker, Kubernetes, OpenShift, and container orchestration.
• Knowledge of relational databases, NoSQL databases, caching technologies, and data integration patterns.
• Experience designing secure applications using OAuth 2.0, OpenID Connect, JWT, encryption, identity management, and role-based access controls.
• Experience with CI/CD tools and DevOps practices using Jenkins, GitHub Actions, GitLab CI, Azure DevOps, Terraform, or similar technologies.
• Understanding of generative AI, large language models, AI agents, prompt engineering, embeddings, vector databases, and retrieval-augmented generation.
• Experience integrating AI services through platforms or frameworks such as OpenAI, Azure OpenAI, Amazon Bedrock, LangChain, LlamaIndex, or Semantic Kernel.
• Strong understanding of system scalability, performance optimization, resiliency, observability, and disaster recovery.
• Excellent communication, stakeholder management, documentation, and technical leadership skills.

Preferred Qualifications

• Experience with Python and AI application development.
• Experience building enterprise AI assistants, intelligent agents, or knowledge-based applications.
• Familiarity with machine learning operations, model evaluation, model monitoring, and AI governance.
• Experience with Snowflake, Databricks, data lakes, or modern enterprise data platforms.
• Knowledge of financial services, retirement services, wealth management, insurance, or other regulated industries.
• Cloud architecture or AI-related certifications are preferred

Technical Skills

Programming and Frameworks:
Java, Spring Boot, Spring Cloud, Python, REST APIs, GraphQL, Microservices

Cloud Platforms:
AWS, Microsoft Azure, Google Cloud Platform

AI and Generative AI:
Large Language Models, AI Agents, RAG, Prompt Engineering, Embeddings, Vector Databases, OpenAI, Azure OpenAI, Amazon Bedrock, LangChain, LlamaIndex

Containers and DevOps:
Docker, Kubernetes, OpenShift, Jenkins, GitHub Actions, GitLab CI, Terraform, CI/CD

Integration and Messaging:
Kafka, MQ, Event-Driven Architecture, API Gateways, Enterprise Integration Patterns

Databases and Data Platforms:
Oracle, PostgreSQL, SQL Server, MongoDB, DynamoDB, Redis, Snowflake, Databricks

Security and Observability:
OAuth 2.0, OpenID Connect, JWT, IAM, Splunk, Dynatrace, AppDynamics, CloudWatch

Education

Bachelor’s or master’s degree in computer science, information technology, engineering, or a related discipline

Key Skills

Education

Bachelor's or Master's degrees