Lead Software Engineer Java Spring boor Gen AI

Own end-to-end solution architecture across applications, APIs, data, integrations, and environments (dev → prod).

Convert business requirements into target-state architecture, HLD, and LLD with clear trade-offs and documented decisions.

Define integration patterns and cross-service contracts (REST/gRPC, event-driven), including versioning, compatibility, and SLAs.

Provide hands-on technical leadership by contributing Java/Spring Boot and Python code for reference implementations, critical paths, and POCs to production.

Lead design spikes and performance investigations to validate approaches and resolve complex technical risks.

Drive complex production issue triage and root cause analysis, ensuring sustainable fixes and operational learning.

Establish and maintain engineering standards through reusable patterns, templates, and shared components.

Design and deliver production patterns for AI/ML capabilities (batch + real-time inference, feature pipelines, evaluation, monitoring).

Partner with Data Science teams to move models from experimentation to reliable, scalable, observable services.

Implement responsible AI controls (traceability, testing/evaluation, approvals, and appropriate human oversight where required).

Define and deliver NFRs and operational excellence (availability, latency, throughput, scalability, resiliency, RTO/RPO, capacity planning, observability/SLOs/runbooks) with secure-by-design and SDLC/audit compliance.

Formal training or certification on software engineering concepts and 9+ years applied experience

Strong hands-on experience designing and delivering enterprise solutions end-to-end.

Java 11/17+ expertise, including Spring Boot microservices, API design, and testing practices.

Python 3.x expertise for services, automation, and data/ML integration.

Proven distributed systems experience: microservices, event-driven architecture, and messaging/streaming (Kafka or equivalent).

Strong data architecture fundamentals: relational/NoSQL patterns, caching (e.g., Redis), data consistency, and schema evolution.

Practical experience productionizing AI/ML: inference patterns, model packaging/serving, and monitoring/drift fundamentals.

Working knowledge of MLOps concepts and operational model lifecycle management.

DevOps delivery mindset: CI/CD, automated testing (unit/integration/contract), and release/rollback strategies.

Strong security and resiliency mindset suitable for regulated environments. Excellent communication and influence skills—able to explain trade-offs and align stakeholders.

Kubernetes/container platforms and cloud-native patterns (autoscaling, config/secrets, service-to-service security).

GenAI experience (LLMs, RAG, vector search, evaluation frameworks, prompt/model governance) where applicable.

Modernization experience (monolith decomposition, strangler patterns, incremental migration).

Advanced production readiness/observability practices (SRE-style monitoring, readiness reviews, operational rigor).