Vetted Python Professionals

Tolulope Akinlabi

Screened ReferencesStrong rec.

Mid-level Cyber & Cloud Security Analyst specializing in AI/ML and cloud risk

Lanham, MD6y exp

“Built a production AI security compliance assessment system using the OpenAI API that ingests company policy documents, performs RAG over embeddings stored in Supabase/FAISS, and generates executive-level gap and maturity reports mapped to NIST CSF, SOC 2, and PCI DSS. Also developed a multi-agent trading assistant orchestrated with LangChain, combining live market data (Yahoo/Polygon.io), sentiment/technical indicators, LSTM-based forecasting, and LLM-generated recommendations.”

“Built end-to-end AI analytics experiences spanning React/TypeScript, serverless APIs, and Postgres, with a strong focus on streaming UX, observability, and reliability. Stands out for turning ambiguous AI product ideas into shippable MVPs, then abstracting repeated patterns into reusable orchestration and multi-tenant configuration systems that improved speed, consistency, and maintainability.”

“Google Health engineer who owned and shipped an AI-powered clinical insights dashboard and NLP clinical note extraction service end-to-end (React/Next.js frontend; Python/Node microservices on GKE; TensorFlow transformers; BigQuery analytics). Demonstrated strong production rigor (CI/CD, testing, observability, guardrails for sensitive data) and delivered measurable outcomes including 30% faster diagnostics, 40% less manual documentation, 15% higher adoption, and 25% lower ops costs.”

“LLM/agentic systems practitioner who partners directly with customers to productionize prototypes end-to-end—defining business-aligned metrics, building evaluation datasets, and shipping monitored, cost-bounded inference APIs on AWS Lambda. Notably delivered a vehicle damage classification system that cut manual review by 40% and stabilized agent workflows by instrumenting state transitions to uncover and fix a race-condition-driven skipped tool call.”

“Senior engineering/technology leader who has defined and delivered a multi-year roadmap to modernize platforms and embed AI, leading global teams through cloud-native and microservices migrations on AWS/Azure. Demonstrated measurable outcomes including 99.99% uptime, 40% fewer incidents, 25% faster delivery, 5x scalability, and $30M in new business opportunities, while scaling a 100+ person distributed org with strong OKR-driven execution and mentorship culture.”

Nicholas Ogura

Screened ReferencesStrong rec.

Executive Platform & Security Engineering leader specializing in multi-cloud Kubernetes and FinTech

San Diego County, USA18y exp

“Startup-focused infrastructure/security leader who stepped into head of engineering and re-platformed an entire product end-to-end in 3 months to meet launch. In crypto/fintech, recognized the market-data system as an ETL/data product and rebuilt it as a separable, securely accessible platform—prompting inbound interest within a week—while advocating an open-source-first observability stack (Prometheus/Grafana/Loki) to avoid vendor lock-in.”

Denis Siminiuc

Screened

Junior Research Assistant specializing in LLMs, NLP, and data systems

Cambridge, MA3y exp

“Software-focused candidate who built a data monitoring pipeline during a hedge fund internship, integrating real databases and an email API to notify teams when data was ready. Comfortable working through legacy/scrappy code and uses LLMs to accelerate comprehension and delivery, with an emphasis on thorough testing and clear communication with stakeholders/customers.”

“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”

Brian Gomez

Screened

Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision

United States12y exp

“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”

“Backend/data engineer with production experience at Uber building a near real-time driver rewards service on AWS (FastAPI, PostgreSQL, Redis) with strong reliability and concurrency controls. Also delivered AWS Lambda/ECS containerized deployments with GitHub Actions CI/CD and cost governance, built AWS Glue ETL with schema-evolution handling, and drove a ~10x SQL performance improvement while owning incident response via CloudWatch.”