Amine Kili

• Led technical strategy and product roadmap as CTO; managed cross-functional teams in engineering, data science, and operations; represented the company at Station F

• Architected an LLM-agnostic multi-agent system using langchain4j with dynamic model selection, achieving response times <200ms

• Built scalable knowledge graphs with Neo4j, implementing semantic search for multi-tenant contextual retrieval

• Provisioned multi-region AWS and Azure cloud infrastructure: VPCs, subnets, security groups, load balancers (ALB/NLB), route tables

• Designed fault-tolerant architecture with Resilience4j circuit breakers, exponential backoff retries, graceful degradation, health checks for zero-downtime deployments

• Architected data layer with PostgreSQL for transactions, MongoDB with sharding for documents, Redis for sub-millisecond caching with TTL eviction

• Developed Spring Boot microservices with REST APIs, dependency injection, aspect-oriented programming, custom exception handling

• Implemented observability with Prometheus (JVM, business metrics), Grafana dashboards, Datadog APM for distributed tracing

• Built CI/CD pipelines with GitHub Actions: unit/integration/e2e tests, Trivy security scan, Docker builds, blue-green deployments to AWS ECS

• Designed multi-tenant IAM with Keycloak OAuth2/OIDC, RBAC policies, tenant isolation, PCI DSS and SOC 2 compliance

Technologies: Kotlin, Java, Spring Boot, langchain4j, LLMs (GPT-4, Claude, Llama), Neo4j, PostgreSQL, MongoDB, Redis, Vector DBs (Azure AI Search, Weaviate), Keycloak, AWS (ECS, EC2, RDS, ElastiCache, S3, CloudWatch, ALB), Azure (VM, CosmosDB), Docker, Prometheus, Grafana, Datadog, ELK, Mixpanel, GitHub Actions

• Led engineering team through scaling phase; managed technical hiring, onboarding, and mentorship; partnered with leadership on technical roadmaps

• Architected distributed microservices platform with Java and Spring Boot: domain-driven design, REST APIs with OpenAPI, asynchronous processing via AWS SNS/SQS

• Built MongoDB data layer with sharding for horizontal scalability, aggregation pipelines for analytics, optimized indexes reducing latency from seconds to milliseconds

• Implemented saga pattern for distributed transactions with compensating logic, idempotent APIs with deduplication for exactly-once semantics

• Deployed AWS X-Ray distributed tracing with correlation IDs, trace sampling, service dependency maps, reducing MTTR by 70%

• Built crypto ledger-based payment system with PostgreSQL ACID transactions, event sourcing audit trails, webhook processing with retry logic

• Optimized analytics for billions of records: MongoDB aggregation pipelines, AWS Lambda batch jobs, materialized views, composite indexes reducing latency by 85%

• Developed ML fraud detection with scikit-learn feature extraction, real-time scoring <50ms, threshold-based alerts reducing false positives by 40%

• Implemented Redis ElastiCache with cache-aside pattern, TTL invalidation, cache warming maintaining p99 latency <100ms, reducing DB load by 70%

• Built CI/CD with GitHub Actions: unit/integration/e2e tests, multi-stage Docker builds, AWS ECS deployments, blue-green releases with automated rollbacks

Technologies: Java, Spring Boot, MongoDB, PostgreSQL, Redis, AWS (ECS, Lambda, SNS, SQS, X Ray, CloudWatch, ElastiCache, RDS, S3, ALB), Docker, GitHub Actions, ELK Stack, Grafana, scikit-learn, Python

• Led architectural design sessions; performed technical feasibility analysis; established code review standards adopted company-wide

• Architected AWS ECS infrastructure with service discovery (Cloud Map), Application Load Balancers, auto-scaling policies achieving 99.9% availability

• Implemented domain-driven design with bounded contexts, event-driven communication via AWS SNS/SQS, CQRS for read/write separation

• Built observability platform: CloudWatch metrics, X-Ray distributed tracing, structured logging, PagerDuty integration reducing MTTR by 60%

• Mentored engineering team through technical interviews, onboarding, knowledge sharing on microservices patterns and AWS best practices Technologies: Java, Python, Spring Boot, AWS (ECS, Lambda, SNS, SQS, Cloud Map, CloudWatch, X Ray), Docker, CI/CD