Senior Java Software Engineer

• Full Stack Development: Design, develop, and maintain scalable full-stack applications with Angular frontends and microservices-based backends, ensuring seamless integration and optimal performance
• API & Microservices Architecture: Build and optimize RESTful and GraphQL APIs, design microservices architectures, and implement efficient inter-service communication patterns
• Generative AI Integration: Architect and implement Generative AI solutions including LLM integration, prompt engineering, RAG (Retrieval-Augmented Generation) pipelines, and AI-powered features into production applications
• Cloud Infrastructure: Design and deploy cloud-native solutions on AWS, leveraging serverless architectures, containerization, and managed services for scalability and reliability
• Database Design & Optimization: Implement efficient database schemas, optimize queries, and manage both SQL and NoSQL databases to support application requirements
• Technical Leadership: Provide technical guidance and mentorship to team members, lead code reviews, establish best practices, and drive architectural decisions
• AI/ML Model Integration: Collaborate with data science teams to integrate ML models, implement model serving infrastructure, and ensure responsible AI practices including bias monitoring and explainability
• Performance & Quality: Ensure applications meet performance benchmarks, implement comprehensive testing strategies, and maintain high code quality standards

• Bachelor's degree in computer science, Software Engineering, or related field (Master's preferred)
• 7+ years of software engineering experience with full-stack development
• Frontend Expertise: 3+ years of production experience with Angular (latest versions), TypeScript, RxJS, NgRx/state management, and responsive UI design
• Backend Expertise: Strong proficiency in Java and/or Python for API and microservices development
• API Development: Proven experience designing and implementing RESTful APIs and/or GraphQL services
• Cloud & DevOps: Hands-on experience with AWS services (Lambda, ECS/EKS, API Gateway, S3, RDS, DynamoDB, etc.) and containerization (Docker, Kubernetes)
• Database Proficiency: Experience with both relational (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) databases
• Generative AI Experience: 1+ years working with LLMs (OpenAI, Anthropic, AWS Bedrock), prompt engineering, vector databases, and embedding models

Preferred Qualifications

• Experience with LangChain, LlamaIndex, or similar LLM orchestration frameworks
• Background implementing RAG architectures with vector databases (Pinecone, Weaviate, pgvector, OpenSearch)
• Knowledge of fine-tuning techniques, model evaluation, and AI safety practices
• Experience with real-time data processing and streaming architectures (Kafka, Kinesis)
• Familiarity with event-driven architectures and asynchronous messaging patterns
• Understanding of security and compliance requirements in regulated financial environments
• Experience with microservices patterns (circuit breakers, service mesh, distributed tracing)
• Contributions to open-source projects or technical publications in AI/ML domains

Skills & Competencies

• Full Stack Mastery: End-to-end ownership of features from UI to database, with deep understanding of frontend-backend integration patterns
• Architectural Thinking: Ability to design scalable, maintainable architectures that balance business needs, technical constraints, and future growth
• AI/ML Integration: Practical knowledge of integrating Generative AI capabilities into production systems, including handling latency, costs, and reliability challenges
• Technical Problem-Solving: Strong debugging and troubleshooting skills across the full technology stack, including AI model behavior and performance issues
• Collaboration & Communication: Excellent ability to work with cross-functional teams, translate business requirements into technical solutions, and communicate complex concepts clearly
• Pseudo-Lead Capabilities: Self-motivated to drive initiatives, mentor peers, facilitate technical discussions, and influence technical direction without formal management responsibilities
• Quality & Testing Focus: Strong commitment to automated testing (unit, integration, e2e), code quality, and continuous improvement
• Learning Agility: Rapid adoption of new technologies and frameworks, particularly in the fast-evolving AI/ML landscape

Key Technologies:

• Frontend: Angular (16+), TypeScript, RxJS, NgRx, HTML5/CSS3, JavaScript
• Backend: Java (Spring Boot), Python (FastAPI, Flask), Node.js
• APIs: RESTful services, GraphQL (Apollo), gRPC
• Generative AI: AWS Bedrock, OpenAI API, LangChain, vector databases, embedding models, prompt engineering frameworks
• Databases: PostgreSQL, MongoDB, DocumentDB, DynamoDB, Redis, Vector databases (pgvector, OpenSearch)
• Cloud Platform: AWS (Lambda, ECS/EKS, API Gateway, S3, RDS, DynamoDB, Bedrock, SageMaker, CloudWatch)
• Microservices & Integration: Docker, Kubernetes, service mesh, API Gateway, message queues (SQS, SNS, Kafka)
• DevOps & CI/CD: GitLab CI/CD, Jenkins, Terraform, CloudFormation, monitoring and observability tools
• Testing: Jest, Jasmine, Karma, JUnit, PyTest, Selenium, Cypress