Aleena Nambipunnilath Alikahn

Enterprise ERP platform (Finance, HRMS, Resource Management) serving live UAE business clients
▸ Architected and delivered a multi-module enterprise ERP system from ground up — owning the full
API lifecycle across design, development, testing, deployment, and production monitoring for Finance,
HRMS, and Resource Management modules.
▸ Developed high-performance async REST endpoints using FastAPI with asyncio, handling concurrent
request loads across multi-tenant ERP modules with non-blocking I/O patterns.
▸ Implemented JWT-based authentication with OAuth2 authorization flows and API gateway-level
security controls, enforcing token validation and rate limiting across all external-facing services.
▸ Reduced API latency under production load by restructuring complex PostgreSQL and MSSQL queries,
redesigning indexing strategies, and eliminating N+1 patterns across high-traffic endpoints.
▸ Designed and implemented a Role-Based Access Control (RBAC) system with granular permission
layers and AES-256 data encryption, bringing the platform to financial-grade security standards required
by UAE enterprise clients.
▸ Built event-driven distributed system components using Apache Kafka, enabling real-time data
streaming and fully decoupled inter-service communication across ERP modules, significantly
improving system resilience and scalability.
▸ Integrated Celery with Redis to handle asynchronous workloads — including report generation,
scheduled batch operations, and multi-channel notification pipelines — offloading time-sensitive jobs
from the main request cycle.
▸ Designed and maintained RESTful APIs following OpenAPI/Swagger standards; ensured backward
compatibility across versioned endpoints serving multiple enterprise clients.
▸ Contributed to CI/CD pipelines using Docker and AWS infrastructure for containerised deployment;
maintained production uptime across cloud-hosted ERP environments.
▸ Enforced engineering quality through Git-based branching workflows, structured pull request reviews,
and internal coding standards — maintaining a clean, maintainable codebase across the team.
▸ Collaborated directly with UAE enterprise stakeholders to translate business requirements into backend
architecture decisions, delivering production features on schedule.

Radar-based AI systems for Oil & Gas industry — research to production
▸ Led R&D on radar-based people tracking for Oil & Gas field deployments; improved ML detection
models to reduce false-positive rates by ~40%, directly increasing system reliability in challenging field
conditions.
▸ Built and deployed computer vision and ML models on AWS, successfully transitioning research
prototypes into production-grade, maintainable systems with consistent throughput under real-time
inference load.
▸ Designed scalable data pipelines for real-time model inference, supporting production deployments with
stable latency across concurrent sensor streams.
▸ Developed full-stack Django applications integrated with MongoDB for real-time sensor data ingestion
and inference dashboards used by field engineers.
▸ Documented model performance benchmarks and maintained version-controlled experiment tracking to
support reproducible research and client reporting.

▸ Delivered custom Python and MATLAB solutions across signal processing, deep learning, and data
pipeline domains for multiple clients — maintaining high client retention through reliable delivery and
proactive communication.
▸ Built standalone ML models for classification and regression tasks; packaged deliverables with
documentation for non-technical stakeholders.