Victor Achilike

Senior Software Engineer with 10+ years of experience delivering scalable web applications, data platforms, and AIpowered solutions across fintech, insurtech, healthcare, and enterprise SaaS. Strong background in building reliable backend and cloud platforms, as well as modern, user-friendly web experiences. Experienced in bringing data and AI capabilities into real products, including forecasting and language-based systems.

Known for building secure, compliant, and highly available systems in regulated environments, and for working closely with product, design, and business teams to take ideas from concept to production.

OneInc, Senior Software Engineer

• Built event-driven, asynchronous services using Java (Spring Boot), Node.js, Go, and Python (FastAPI) to process high-volume transactions with robust retry mechanisms and fault-tolerant failure recovery.
• Developed Node.js services and REST APIs supporting internal tools, partner integrations, and backend-for-frontend (BFF) workflows for React dashboards.
• Built and maintained React frontend components and dashboards for internal payment operations, integrated with Node.js and Spring Boot microservices.
• Developed secure, versioned REST and GraphQL APIs used by internal platforms and external insurance carriers for real-time payment status and reporting.
• Modernized legacy payment systems into domain-aligned microservices, improving deployment speed, fault isolation, and long-term maintainability.
• Optimized PostgreSQL and MongoDB schemas, indexing, and queries to reduce latency and improve throughput for financial workloads.
• Implemented batch and streaming data pipelines using Kafka, Airflow, SQL, and Python to support payment events, settlement reporting, and analytics.
• Built audit-ready data pipelines supporting compliance, reconciliation, and enterprise customer reporting.
• Improved platform reliability through observability, SLA monitoring, and alerting using Prometheus and Grafana, reducing incident response time.
• Coordinated monthly release cycles across backend, frontend, data, and DevOps teams to ensure on-time, high-quality production deployments.
• Owned release planning, scope alignment, and cross-team communication to reduce last-minute issues and improve release stability.
• Served as the point person for production releases, coordinating testing, rollout, and post-release validation.
• Deployed and operated services on AWS (EKS, RDS, S3) using Docker, Kubernetes, Terraform, and automated CI/CD pipelines.
• Core contributor to a multi-tenant, petabyte-scale data management platform supporting ingestion, governance, discovery, and consumption of multimodal ML datasets (audio, video, images, and tabular).
• Designed and scaled high-throughput ingestion pipelines using Spark, Ray, and Python, eliminating concurrency bottlenecks and enabling large-scale parallel ingestion workloads.
• Migrated hundreds of datasets from legacy systems to DMS, including PII-enabled datasets, with encryption, access control, and compliance guarantees.
• Built and shipped Facets, enabling datasets to be materialized into optimized formats (Parquet, S3, TFDS) for Spark, Ray, and ML training pipelines.
• Delivered 10x–30x performance improvements and 70%–80% cost reductions by replacing legacy data pipelines with DMS-backed ingestion and consumption.
• Coordinated regular platform releases across multiple teams, aligning ingestion, API, and infrastructure changes into stable production rollouts.
• Improved platform reliability by resolving ingestion and consumption failures, leading cross-team RCAs, and hardening APIs, SDKs, and operational monitoring.
• Built and trained speech-to-text (STT) and text-to-speech (TTS) models in TensorFlow using CNN and R-CNN architectures, improving transcription accuracy and voice quality for IVR payment systems.
• Performed hands-on feature engineering and data preprocessing on audio and text data, including normalization, segmentation, and signal-level feature extraction, to improve model quality.
• Reduced overfitting using regularization, dropout, and validation-based tuning, and addressed underfitting by expanding training data and applying data augmentation techniques.
• Iterated on model training using evaluation metrics and real call data, balancing accuracy, latency, and stability for production deployment.
• Developed and maintained internal tools in C++ and C# for audio and text feature extraction, supporting feature engineering pipelines used in STT and TTS model training.
• Evaluated STT and TTS models using accuracy, WER, and latency metrics, and iterated on architectures and training data to improve real-world call performance.
• Set up experiment tracking and model versioning to compare model changes before production deployment.
• Integrated deep learning models using PyTorch and Hugging Face to enable transcription, voice synthesis, and sentiment analysis pipelines.
• Evaluated and integrated LLM-based (GPT) models for intent detection, conversational understanding, and response generation.
• Built low-latency Python inference services and APIs to expose AI capabilities to backend systems.
• Applied sentiment analysis to call transcripts to surface customer intent, frustration signals, and quality metrics.
• Ensured compliance with PCI, Nacha, and security requirements, including tokenization, controlled logging, and PII-safe processing.

Empower, Senior Software Engineer

• Built and maintained React-based micro-frontend applications for retirement account management, financial planning, and participant-facing tools, integrating with backend services via secure REST APIs.
• Built and scaled core retirement recordkeeping systems supporting account balances, contributions, transactions, vesting, and historical financial data.
• Developed financial planning and forecasting services used by millions of participants for retirement projections and scenario modeling.
• Designed transactional backend services using Java, Spring Boot, Hibernate/JPA with strong consistency and auditability guarantees.
• Architected batch and streaming data platforms using Kafka, Spark, Airflow, supporting analytics and regulatory reporting.
• Increased ingestion throughput to 10K+ TPS and processed millions of financial records daily with high accuracy.
• Built secure, multi-tenant SaaS systems with fine-grained access control, encryption, and data governance.
• Implemented data quality validation, lineage tracking, anomaly detection, and SLA monitoring.
• Improved performance and reduced costs by up to 40% through query optimization and workload tuning.
• Partnered with product, compliance, and analytics teams to meet SEC and FINRA regulatory requirements.
• Helped move the platform to safer releases using blue/green and canary deployments, so we could ship changes without taking down critical user flows.
• Built and maintained CI/CD pipelines with GitLab CI and Jenkins, cutting down manual release work and making rollbacks a lot less scary.
• Used Datadog and Splunk day-to-day to track production issues, dig through logs, and spot problems before they blew up.
• Ran load and stress tests with JMeter ahead of big releases to make sure we wouldn’t fall over during peak traffic or large batch runs.
• Spent plenty of time in Linux boxes debugging performance, memory issues, and weird production-only failures in distributed services.
• Shipped changes using staged rollouts and feature flags so we could test in production without putting users at risk.
• Acted as Scrum Master for the team, running sprint planning, daily standups, and retros, and helping remove blockers to keep projects moving.
• Developed and maintained HSA account management features for saving and paying qualified medical expenses.
• Built microservices for account management, investment tracking, and secure messaging.
• Built and maintained internal tools for managing inbox and outbox workflows, renewal requests, using Rust.
• Developed backend services using Ruby on Rails and Java Spring Boot for high-throughput notification processing.
• Built micro-frontend UIs using Vue.js for expense tracking, receipt uploads, and bill payments.
• Integrated with third-party financial and healthcare systems for payroll contributions, and payments.
• Ensured compliance with IRS and healthcare regulations.
• Designed and optimized REST APIs and PostgreSQL schemas for performance and scalability.
• Added end-to-end tests with Cypress for the Vue.js and Rails apps, covering real user flows like receipt uploads, payments, and account updates.
• Wired those tests into CI so broken builds got caught before they made it anywhere near production.
• Implemented model training, validation, and inference pipelines using TensorFlow.
• Integrated forecasting services with a .NET-based backend, enabling consumption.
• Built an Angular-based dashboard to visualize forecasts, confidence ranges, and historical trends.
• Improved model quality through feature engineering, hyperparameter tuning, and evaluation metrics.
• Developed internal workflows, approval systems, and role-based dashboards using PHP and WordPress.
• Built secure knowledge management systems for SOPs and internal documentation.
• Implemented REST API integrations and SSO authentication.
• Optimized access control to protect sensitive operational data.

Bachelor of Science in Computer Science