Solutions Architect

About

Strong and balanced background in Java, Microservices Development and Artificial Intelligence (AI) / Machine Learning (ML). In this critical role, Solutions Architect will be responsible for architecting designing, leading developers, and involved in maintaining robust, scalable, and high-performance Java applications, while also seamlessly integrating cutting-edge AI/ML models or components into our enterprise applications. Solutions Architect will lead technical initiatives and contribute significantly to our architectural strategy and AI-driven solutions within a complex financial services environment.

Key Responsibilities

• Architect and Design: Lead the design and architecture of high-volume, low-latency Java microservice systems and sophisticated solutions for the integration and deployment of AI/ML models.
• Development and Implementation: Develop clean, efficient, and well-documented code for core microservice components, APIs, AI-driven services, and related systems using Java and its ecosystem.
• Performance Optimization: Identify and resolve performance bottlenecks, ensuring optimal performance and scalability of both microservices and AI-integrated components.
• Technical Leadership: Provide technical leadership and guidance to development teams, fostering best practices in software development, AI integration, and microservices architecture.
• Code Quality and Standards: Ensure adherence to coding standards, security policies, and architectural guidelines, performing code reviews and promoting a culture of quality across all development efforts.
• Troubleshooting and Support: Diagnose complex technical issues across the microservice and AI/ML integration stack, providing expert support for critical production systems.
• Collaboration: Work closely with data scientists, AI/ML engineers, front-end developers, and other stakeholders to understand requirements and deliver integrated, intelligent solutions.
• Innovation: Stay abreast of emerging technologies, particularly in the Java ecosystem, microservice patterns, and AI/ML operationalization (MLOps), recommending and implementing innovative solutions to drive business value.
• Documentation: Create technical documentation, including design specifications, API documentation, and operational guides for both microservices and AI/ML components.

Preferred Qualifications

• Experience: +10 years of extensive experience in software development with a strong and proven focus on Java Microservices, enterprise application/ integration, domain driven design, and hands-on experience in using Manifestos, agile, and AI/ML.
• Language and Package Expertise: Expert-level proficiency in Java (JDK 11+) and core Java ecosystem (Spring Boot, Spring Cloud, Hibernate, Gradle), Python, Apache Superset, Anthropic, Open MetaData.
• Microservices & Middleware Technologies: Deep experience with microservices architectures, distributed systems, message brokers (e.g., Kafka, RabbitMQ), and API gateways.
• Spec-Driven Development: Experience with Spec-Driven Development (e.g., OpenAPI/Swagger for API design) to ensure clear contracts and consistent implementation.
• AI/ML: Demonstrated expertise in integrating AI/ML into production systems, including a thorough understanding of model deployment, inference, scalability, agentic AI, generative AI, Vertex AI Gemini Pro, and monitoring challenges.
• Modern Engineering Tools: Lightspeed, Copilot, Github; Embrace AI tools to automate different SDLC phases - Devin, Friday.AI.
• Cloud Platforms: Experience with cloud platforms (e.g., AWS, Azure, GCP, OpenShift/Kubernetes) and containerization technologies (Docker).
• Database Skills: Strong understanding of relational and NoSQL databases (e.g., Oracle, PostgreSQL, MongoDB, Cassandra, Starburst).
• DevOps/MLOps: Familiarity with CI/CD pipelines, MLOps principles, and tools for automating deployment, testing, and monitoring of both software and AI/ML pipelines.
• Problem-Solving: Exceptional analytical and problem-solving skills, with the ability to tackle complex technical challenges in a fast-paced environment.
• Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Leadership: Demonstrated ability to lead technical initiatives, mentor team members, and drive consensus on technical decisions within cross-functional teams.
• Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Other Qualifications

• Experience with specific AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn, particularly in the context of integrating models developed using these tools into Java applications.
• Knowledge of streaming data processing frameworks (e.g., Apache Flink, Spark Streaming) for real-time AI inference.
• Experience in the financial services industry, understanding of regulatory and security requirements related to both software development and AI/ML.
• Contributions to open-source projects or relevant technical communities.