Role Overview:The Senior Java AI Engineer/Architect will own end-to-end architecture, from requirements and constraints through HLD/LLD, implementation, release, monitoring, and production improvement. This role involves designing and delivering secure, high-throughput REST APIs, microservices, and event-driven integrations using Java/Spring Boot, as well as developing production AI capabilities leveraging LLM APIs, RAG, vector search, and agents, with a focus on evaluation, guardrails, access controls, monitoring, and operational support.
Key Responsibilities:- Own end-to-end architecture from requirements and constraints through HLD/LLD, implementation, release, monitoring and production improvement.
- Independently sketch and defend a distributed-system design; articulate assumptions, bottlenecks, trade-offs, alternatives and design decisions.
- Critically review architecture, identify security/scalability/consistency gaps and propose workable remediation with measurable impact.
- Document decisions as ADRs and explain personal design contributions, rejected alternatives and outcomes.
- Design and deliver secure, high-throughput REST APIs, microservices and event-driven integrations using Java/Spring Boot or comparable backend technologies.
- Engineer reliable financial workflows using idempotency keys, transactional boundaries, retries, reconciliation, auditability and controlled recovery from partial failures.
- Design appropriate data models, caching, message processing and fault-tolerant service interactions.
- Design and enforce fine-grained authorization at API, service and resource levels; distinguish authentication, token validation and authorization decisions.
- Apply OAuth2/OIDC, JWT, RBAC/ABAC, least privilege, secure service identity and tenant isolation, including authorization of AI tools and retrieved documents.
- Deliver production AI capabilities using LLM APIs, RAG, vector search and agents where appropriate; own evaluation, guardrails, access controls, monitoring and operational support.
- Explain why an AI architecture was selected, its limitations, security risks, fallback behavior and measurable business results.
- Deploy on AWS, Azure or GCP; use Docker/Kubernetes, automated CI/CD and infrastructure as code.
- Define SLOs, investigate latency/throughput issues, use metrics/logs/traces and lead root-cause analysis and resilience improvements.
- Communicate designs clearly in technical reviews and respond constructively to design challenges.
Required Skills:- Independently design scalable, secure, highly available distributed systems from ambiguous requirements; produce HLD/LLD, API contracts, data models, sequence diagrams and ADRs.
- Explain trade-offs between synchronous/asynchronous communication, database technologies, caching, consistency, throughput, latency and operational cost.
- Identify flaws in a proposed design and propose practical corrections without extensive prompting; explain assumptions, alternatives and failure modes.
- Demonstrated personal ownership of at least one complex production system, including implementation, rollout and incident response.
- Microservices, REST APIs, Kafka or equivalent messaging; idempotency, retries, timeouts, circuit breakers, dead-letter queues, ordering, outbox/Saga patterns and transaction consistency.
- Authentication versus authorization (AuthN/AuthZ), OAuth2/OIDC, JWT validation, RBAC/ABAC, resource-level permissions, least privilege, tenant isolation and service-to-service security.
- Capacity planning, horizontal scaling, performance diagnosis, SLOs/SLIs, tracing, observability, failure recovery, safe deployment and rollback.
- Financial transaction or payments workflow design (ACH, push payments, P2P, settlement, reconciliation) strongly preferred.
- Strong backend programming and hands-on delivery (Java 17/21, Spring Boot, Spring Cloud, Hibernate/JPA preferred).
- Production AI/GenAI integration experience: LLM APIs, RAG, agent/tool orchestration, vector retrieval, evaluation, access control, prompt-injection safeguards and observability.
- SQL and NoSQL database design; cloud (AWS, Azure or GCP), Docker/Kubernetes, CI/CD and automated testing.
Qualifications:- 5-8 years of experience in system design, distributed systems, security, production engineering, and AI.
Preferred Skills:- Kotlin/Python, LangChain/LlamaIndex, MCP, multi-agent systems, GraphRAG, knowledge graphs, MLOps/LLMOps, NVIDIA NIM, Terraform, GKE, Redis, MongoDB, PostgreSQL, DynamoDB and Snowflake.
- Senior technical leadership with demonstrable individual design ownership.
- Structured, concise communication of system diagrams, assumptions, risks, trade-offs and remediation options.
- Ability to reason independently under follow-up questions, acknowledge limitations and improve designs collaboratively.
- Mentoring, peer design reviews, cross-team influence, production accountability and stakeholder engagement.
