Vetted Containerization Professionals

Joshua Smith

Screened ReferencesStrong rec.

Executive engineering leader specializing in AI automation and enterprise transformation

Banks, OR23y exp

“Technology leader with deep accelerator and zero-to-one product experience across the Department of Defense, Fortune 100 enterprises, academia, and GovTech. Most notably, they built a seven-tier solution that generated over $10M in first-year savings and was adopted by 300+ DoD organizations, positioning them as a strong CTO-type operator for mission-driven startups and complex enterprises.”

“Backend engineer currently at Citigroup working on real-time transaction processing systems with Kafka. Stands out for using AI tools pragmatically in a regulated banking environment to improve debugging, testing, and developer productivity while keeping human control over architecture, security, and performance decisions.”

“Fintech startup veteran (8+ years) building TrustRelay, a control-plane product between ERPs and banks to verify vendors, enforce pre-flight payout policies, automate reconciliation, and produce deterministic audit evidence. Currently at MVP stage and planning to pursue Founders Institute Boston Sprint 2026 while lining up design partners and raising pre-seed/seed.”

“Robotics software engineer/researcher focused on perception, SLAM, and sensor fusion, with hands-on experience taking systems from simulation to embedded/real-time deployment. Led transparent-surface (glass) detection using GDNet and achieved a major real-time speedup (~7–9 FPS to ~30 FPS) while preserving >90% recall, and has built ROS-based EKF GPS-IMU fusion plus profiled/optimized Visual SLAM for performance and memory stability. Also brings production-style deployment skills via Docker/Kubernetes orchestration of ML inference services with autoscaling and model update rollouts.”

“Master’s student and Deep Learning teaching assistant who teaches LLM/VLM fine-tuning (including LoRA) and built a Hugging Face LLM fine-tuned for unit conversion, improving reliability by analyzing synthetic data and filling missing number-system conversion examples. Also implemented the Raft consensus protocol using gRPC in a distributed systems course with correctness validated by unit tests.”

“LLM/agentic workflow specialist with healthcare deployment experience who has taken LLM-based automation from prototype to production using operator-in-the-loop validation, RAG-style retrieval, RBAC, and monitoring for sensitive data compliance. Demonstrated real-time incident resolution (retrieval timeouts due to network/proxy misconfig) and strong GTM support—hands-on developer workshops and sales demos translating technical safeguards and real-time ETL into measurable ROI (70% ops reduction, ~$200K/year savings).”

“Backend engineer at Cerebrone.ai building cloud-native Flask microservices for an AI-driven automation platform on GCP (Cloud Run/App Engine), including dedicated inference services integrating OpenAI and internal ML pipelines. Demonstrated strong performance and scalability wins across Postgres/SQLAlchemy optimization, multi-tenant (healthcare/HIPAA-grade) data isolation, and high-throughput background processing with Celery/Redis/RabbitMQ, with multiple quantified latency/CPU/throughput improvements.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”

“Backend engineer on Morgan Stanley’s trade risk and compliance platform, building Java/Spring Boot microservices that validate equity and fixed-income trades at multi-million-events/day scale. Shipped an LLM-assisted trade exception analysis feature using RAG over internal policy documents and trade history, with production-grade guardrails (confidence thresholds, audit logs, human-in-the-loop) and measurable performance wins (~30–35% faster reporting) through PostgreSQL tuning and Redis caching.”

“Customer-facing implementation/security-focused professional with experience at Freshworks and IMImobile (Cisco), advising enterprise customers on app security for CRM/ITSM and sensitive/PII workflows. Has led threat-model-driven remediation for API/RBAC/token risks and troubleshot complex cloud integration incidents (token expiry + load balancer misrouting) using logs/metrics/tracing, escalating with clear technical reports to accelerate engineering fixes.”

“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”

“Azure cloud platform engineer with strong enterprise Linux operations background who designs multi-region HA/DR on Azure (and AWS) using Azure Site Recovery, Traffic Manager, AKS autoscaling, and geo-replicated Azure SQL. Built secure Azure DevOps CI/CD pipelines for .NET/Python microservices to AKS/VMs and provisions full environments via Terraform modules with remote state, drift checks, and staged rollouts; has not directly owned IBM Power/AIX at scale.”

“Internship at ActiveVM where they tackled large-scale Spring Boot 2→3/library migrations across hundreds of downstream products by combining OpenRewrite (AST-based recipes) with an LLM/RAG-based classifier that routed risky files to human experts. Reported ~70% reduction in manual effort and 90%+ accuracy after testing across multiple branches and cutovers; also built a CTR-driven book recommendation capstone showcased at the Google office in Cambridge.”

“Engineer with production experience in both high-throughput banking risk systems and LLM agent platforms. Built a real-time transaction risk scoring middleware at JPMorgan Chase (1M+ requests/day) emphasizing HA, observability, and audit/PII compliance, and also architected multi-step LLM agents with strict schema-based tool calling, evaluation loops, and safety guardrails for messy enterprise data.”

“Data engineer in healthcare (HCA) who owned end-to-end Azure-based pipelines at very large scale (50M+ daily claims/patient records). Strong focus on reliability: schema-drift fail-fast validation, quarantine layers, and Python/SQL data quality checks that reduced issues ~25%, plus performance tuning in Databricks/PySpark and versioned serving in Synapse for downstream consumers.”

Ashank Dsouza

Screened

Mid-level Full-Stack Developer specializing in Node.js/React and cloud DevOps

Bengaluru, India4y exp

“Software engineer with startup and capstone experience who improved an ~8-hour database refresh workflow by moving API calls to asynchronous execution and then addressing API rate limits via throttling. Emphasizes performance profiling/logging, strong developer onboarding documentation practices, and disciplined Agile/Jira bug triage and expectation-setting with stakeholders.”

Nikitha Kommidi

Screened

Mid-level AI/ML Engineer specializing in fraud detection, NLP, and MLOps

6y exp

“Built a production real-time fraud detection and customer-support automation platform at Citibank, tackling extreme class imbalance (reported ~1:5000) and strict latency constraints. Combines hands-on MLOps (Airflow, Kubernetes, MLflow; Snowflake/Spark/S3 integrations; CI/CD model promotion) with cross-functional delivery to Risk & Compliance focused on interpretability and reducing false positives.”

Edward Yu

Screened

Senior Software Engineer specializing in FinTech, payments, and authentication

Sunnyvale, CA8y exp

“Software engineer with experience at Dollar Shave Club and Mudflap, focused on user-driven product delivery and cross-team alignment. Built an Okta SSO-based internal portal (React/Node) centralizing access to org services, and has hands-on experience diagnosing LLM/agent workflow issues via instrumentation and systematic triage. Known for translating between product/customer needs and engineering tradeoffs to ship quickly while planning stabilization and scalability.”

Vikram Kini

Screened

Mid-Level Full-Stack Engineer specializing in React, TypeScript, and microservices

3y exp

“Built and productionized an AI agent-based in-app assistant at ISAFE to guide users through document workflows, piloting with a partner school district and then rolling out across districts. Combines hands-on LLM/agent debugging (logs, fallback rates, state/context tracking) with strong technical demos and sales enablement through live workflows and pilot programs (e.g., Osceola School District).”