Vetted Kubernetes Professionals

“Built a production assistive-vision iPhone app to help visually impaired users find grocery items, training a custom YOLO detector on 2,000+ self-collected/annotated images and deploying via CoreML with a cloud multimodal LLM for navigation instructions. Brings hands-on AWS serverless + ECS container deployment (CDK/GitHub Actions) and a disciplined approach to AI workflow reliability (state-machine design, offline evals, stress tests, logging/metrics), plus experience communicating model insights to non-technical stakeholders (MOTER Technologies).”

“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”

“ML/LLM engineer who has shipped a production transformer-based document understanding system on AWS, owning the full pipeline from domain fine-tuning to Dockerized CI/CD deployment. Demonstrates strong production rigor—latency optimization (distillation/quantization, async batching, autoscaling), orchestration with Airflow/Step Functions/Azure Data Factory, and monitoring/drift detection—plus experience translating ops stakeholder needs into adopted AI automation via dashboards.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

“AI/LLM engineer with Fidelity Investments experience who built and shipped a production GraphRAG system that augmented prompts with codebase context, improving business analyst efficiency by 15% and saving ~$3.5M annually. Strong in AWS EKS/Kubernetes/Helm and enterprise IAM/OIDC patterns (including cross-account S3 access), with experience mentoring interns and collaborating with non-technical leaders to extend AI pipelines (e.g., adding SQL functionality during MVP).”

“Generalist software engineer with recent contract work at Vertex Pharmaceuticals shipping a desktop-integrated RAG assistant for lab scientists (2000+ pages ingested; ~40% support-ticket reduction in pilot). Previously owned Python/AWS financial automation services at Amazon operating at multi-billion-dollar scale, with strong strengths in API design, observability, and database/performance tuning; also built a React/TypeScript AI contract analysis product (ContractsGuy).”

“Backend-focused full-stack engineer who built an MVP at Neon AI for PhD students: a FastAPI backend integrating multiple cloud and local LLMs plus a RAG pipeline with session/identity management, designed to be modular and extensible across domains. Also has VMware experience debugging production issues and executing safe, API-compatible refactors with staged rollouts and strong security controls.”

“AI/full-stack engineer with experience delivering production-critical healthcare AI at Roche (digital pathology imaging platform using Microsoft Gigapath for cancer diagnosis) and building scalable LLM-backed products. Strong in designing async AI backends (Django/Celery/Postgres/Redis on GCP), reliability engineering (Datadog, incident response), and agent-style document analysis workflows with evaluation loops.”

“Software engineer who owned an end-to-end self-service reporting workflow (secure APIs, async/batched processing, and React UI), improving report generation performance by ~30–40% and reducing manual support effort. Also built a RAG/embeddings prototype over internal docs and service logs with grounding-focused guardrails, and has a strong reliability/observability mindset (retries, DLQs, CI/CD validation, dashboards/tracing) for distributed workflows.”

“Associate Director in commercial technology leading Salesforce platform delivery (Sales Cloud + Health Cloud) for patient engagement and order management. Personally led secure integrations like Bartender Cloud label/barcode generation (PDF creation, encryption, malware scanning) and owned a major StreamSets-based Salesforce data sync incident triggered by a Salesforce region move, adding proactive monitoring and automated DR/failover. Experienced in scaling delivery via CI/CD, release cadence, and leading teams through architecture reviews, code reviews, and lead-to-cash automation.”

“Senior engineering leader with experience at Disney and BlackLine who drives business-aligned technology roadmaps through deep Product/Engineering partnership (two-in-a-box) and pragmatic prioritization frameworks. Has led major modernization initiatives—private-to-public cloud migration to GCP with multi-cloud evolution, data-layer performance improvements (Mongo/Redis, caching/query optimization), and tooling upgrades (VSS to GitHub)—while scaling teams with strong quality and accountability culture.”

“Engineering leader (player-coach) recently at Autodesk driving a major sales-motion transformation spanning account hierarchy, commissions/quotas, and downstream financial/sales forecasting impacts. Led cross-functional design with enterprise architects and shipped the end-to-end release, using POCs and Anaplan what-if modeling to validate a risky hierarchy change while coordinating delivery across India/Singapore teams and instituting structured Jira-based tech debt/support tracking and automation (dbt, GitHub Copilot).”