Vetted PostgreSQL Professionals

“Applied ML to physical EV fleet systems at ST Labs, building a real-time CNN-LSTM fault prediction pipeline from streaming vehicle telemetry and addressing live data alignment issues via resampling/interpolation and buffered inference. Also developed a V2G/G2V energy transfer algorithm to automate charging/discharging for profit optimization, and made high-impact low-latency pipeline decisions at Astera Holdings using profiling, replay testing, and live A/B validation.”

“McKinsey data/ML practitioner who led production deployment of an entity resolution + semantic search platform for unstructured finance and healthcare data, integrating with legacy systems under HIPAA constraints. Deep hands-on stack across transformers (spaCy/HF BERT), embeddings + FAISS, and production MLOps/workflow tooling (Airflow, Docker, CI/CD, Prometheus/Grafana), with reported gains of +30% decision speed and +25% search relevance.”

“Backend engineer who built and owned an AI-powered SMS survey platform for a nonprofit serving at-risk communities (internet-limited users), using Cloudflare Workers + Twilio and a state-machine survey engine. Scaled it to ~10k active users with near-zero downtime, added English/Spanish support, and iteratively improved LLM behavior (Claude 3.7 Sonnet) to handle nuanced, real-world SMS responses reliably.”

“Engineering leader/player-coach who helped ship a web-based ERP SaaS release (Nov 2025) as part of a long-term migration from a legacy desktop ERP, designing a multi-API architecture (Oracle + EF Core, caching, integrations) and enforcing rigorous code review quality gates. Previously led development of a low-latency, multi-service high-frequency trading platform at a startup hedge fund (Capitalogix Trading), leveraging async/multithreading, event-driven messaging, NoSQL, and WebSockets.”

“Full-stack engineer with end-to-end ownership experience, including building a real-time campaign/inventory dashboard at P&G using React/TypeScript, Spring Boot, GraphQL/REST, Redis, Docker, and AWS (EC2/RDS/S3) with Prometheus/Grafana observability. Demonstrates strong performance and reliability focus (p95 tuning, caching, idempotent event-driven ingestion with DLQs/reconciliation) and has shipped MVPs in ambiguous early-stage environments.”

“ML/AI engineer with production experience across retail and healthcare: built a real-time computer-vision shelf monitoring system at Walmart and optimized edge inference latency by ~30% using TensorRT/ONNX and pruning. Also partnered with CVS Health clinical/pharmacy teams to deliver a medication-adherence predictive model, using Streamlit explainability dashboards and achieving an 18% adherence improvement.”

“AI/software engineer with experience at Visa building a real-time transaction fraud/risk scoring microservice in the card authorization path (Python, Kafka, Kubernetes on AWS) with strict 120–150ms latency constraints and reason-code outputs for downstream decisioning. Owns ML backend end-to-end (data/feature engineering, model training, deployment) and has demonstrated production reliability work including latency spike mitigation, SLO-based observability, drift monitoring, and safe fallbacks to rule-based decisions.”

“Siemens AI engineer who shipped production multi-agent LLM systems across cybersecurity and sustainability, including a vulnerability automation agent that cut manual work 70%. Deep in orchestration (LangGraph supervisor-worker state machines), reliability engineering (async fault tolerance, retries, spike handling), and rigorous evaluation (offline benchmarks, LLM-as-a-Judge improving label agreement 28.9%) with measurable production guardrails.”

“Built and owned "Cortex," an AI agent that helps users understand large GitHub repositories by mapping architecture and relationships between files/folders in minutes. Implemented an agentic, multi-stage prompt decomposition approach and validated it across open-source repos, while also doing legacy service modernization work involving dependency upgrades and refactors.”

“Financial-services AI engineer who shipped a production investment research assistant using RAG over internal research reports, SEC filings, and meeting transcripts, with a strong emphasis on truthfulness and guardrails. Built a structured evaluation loop (200+ golden test cases, RAG Triad metrics) that directly improved retrieval quality (e.g., fixing year-mismatch retrieval, boosting sensitive-query performance by 18% and cutting hallucinations to near zero) and scaled ingestion to ~10k messy documents with RabbitMQ + OpenTelemetry.”

“Full-stack engineer with production experience building a security integrations portal in Next.js (App Router + TypeScript), using server components and typed route handlers as a secure proxy to multiple third-party security vendors. Demonstrated ability to scale performance significantly (server-side re-architecture for 1M+ datapoint dashboard filtering; Postgres query tuning from 1–2s to <200ms) and to own features post-launch (reliability, caching/background sync, and rapid onboarding of new integrations) in a pre-Series B through post-Series C environment.”

“ML/Backend engineer with ServiceNow experience building production-grade inference services on FastAPI with Docker/Kubernetes (autoscaling, health checks) and strong reliability practices (monitoring, retries/timeouts, fallbacks). Delivered measurable improvements including 30% lower API latency and 18% higher model accuracy, and built A/B testing plus drift-triggered retraining loops to keep models stable in production.”

Srinivas Matta

Screened

Mid-Level Full-Stack Software Developer specializing in cloud-native web platforms

Paducah, KY4y exp

“Software engineer at Capital One who owned and shipped AI-driven personalization and internal insights dashboards end-to-end, emphasizing fast iteration with feature flags and tight user feedback loops. Built a TypeScript/React + Spring Boot/Python document automation platform with compute-heavy NLP microservices, async workflows, and production-scale reliability/performance practices (Kafka/RabbitMQ-style queues, Redis caching, tracing).”

Wei Jiang

Screened

Junior Machine Learning Engineer specializing in MLOps and statistical modeling

Greenwood, SC3y exp

“Integration engineer at ES Foundry who led deployment of ELsentinel, a production EL image-based solar cell quality monitoring system using a Swin Transformer classifier (>0.8 F1 across 15+ classes) plus a live real-time prediction dashboard. Strong in solving messy labeling/data-quality problems with process-team collaboration and shipping ML systems despite limited compute/infrastructure.”

John Chen

Screened

Junior Full-Stack & Data Scientist specializing in ML/NLP and analytics products

Redwood City, CA2y exp

“Built and deployed profitprops.io, a sports betting player-props prediction product using ML/AI. Implemented backend APIs with FastAPI/Express.js and Supabase, trained models on AWS GPU (P3) using Docker + RAPIDS, and set up CI/CD with GitHub Actions while working around cost constraints and data-collection hurdles (EC2 proxy rotation/rate limits).”

Monish Sri Sai Devineni

Screened

Mid-level Machine Learning Engineer specializing in financial AI, NLP, and MLOps

Boca Raton, FL5y exp

“AI/ML engineer with experience at Accenture and Morgan Stanley, building production LLM systems (GPT-3 summarization) and finance-focused ML models (credit risk and trading anomaly detection). Combines MLOps depth (Docker/Kubernetes, AWS SageMaker/Glue/Lambda, MLflow, A/B testing, drift monitoring) with practical domain adaptation techniques like few-shot prompting and RAG/knowledge-base integration.”

SAI RAKAM

Screened

Mid-Level Software Engineer specializing in backend microservices and FinTech payments

Remote, USA3y exp

“Capital One engineer focused on fraud and payments platforms, owning end-to-end services and internal tools used by fraud analysts. Built high-traffic Kafka/REST systems and real-time React/TypeScript dashboards (WebSockets, Redis), with strong emphasis on observability, idempotency, and scalable microservices. Successfully drove adoption of AI-assisted fraud classification by pairing transparency and manual overrides with measurable workflow improvements.”

Sayali Patil

Screened

Mid-level Python Full-Stack Developer specializing in Healthcare and FinTech

Everett, MA6y exp

“Backend engineer with hands-on experience building a fraud-transaction monitoring system in Python/Flask, architected as Dockerized microservices and integrated with Kafka for high-volume streaming. Demonstrates strong performance and reliability chops across PostgreSQL/SQLAlchemy tuning (EXPLAIN ANALYZE, N+1 fixes, bulk ops), multi-tenant data isolation, and scaling via background workers + Redis caching, plus real-time ML inference deployment using TensorFlow on AWS.”

Junhui Huang

Screened

Intern Machine Learning Engineer specializing in LLMs, MLOps, and NLP

Providence, RI1y exp

“Built and deployed a production LLM-driven Dungeons & Dragons game where the model acts as a dungeon master, adding a structured combat system and a macro-state tree to ensure campaigns converge to a clear ending. Fine-tuned Gemini 2.5 Flash on Vertex AI and deployed on GCP with Kubernetes, using RAG over DnD rules/spells plus multi-agent orchestration (intent-based routing between narrative and combat agents) to reduce hallucinations and improve reliability.”

Vaibhav Sharma

Screened

Mid-level Software Engineer specializing in AI/ML and data platforms

Remote, USA5y exp

“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”

Alexey Elkin

Screened

Junior Full-Stack Software Engineer specializing in cloud analytics and web applications

New York, NY2y exp

“LLM/agentic workflow engineer with hands-on experience turning demo-grade LLM analytics into production-ready features by tackling tail latency, observability, and cost/reliability controls. Strong at diagnosing real-time customer incidents via trace-driven debugging across retrieval, tool calls, retries, and prompt/version metadata, and frequently partners with sales as a technical lead to de-risk enterprise pilots through transparent failure-mode walkthroughs.”

Prasannakumar B Vardi

Screened

Senior Software Engineer specializing in low-latency ad targeting and distributed backend systems

Santa Clara, CA9y exp

“Backend/platform engineer who built a high-scale audience segmentation and real-time targeting system using Spark/Glue + S3/Hudi and low-latency API services backed by Redis/relational stores. Demonstrates strong production rigor: Spark performance tuning to eliminate OOM failures, API idempotency/caching to cut p95 latency ~40%, and careful dual-run/feature-flag migrations with reconciliation and rollback runbooks. Experienced implementing layered security with JWT/OAuth, RBAC/ABAC, and database row-level security to prevent privilege escalation.”

Kanaka Chalam Volety

Screened

Staff DevOps/SRE Engineer specializing in AWS, Kubernetes, and GitOps

San Jose, CA24y exp

“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”

Frank Meadows

Screened

Senior QA Analyst specializing in streaming video, Connected TV, and data annotation

Remote10y exp

“QA professional with experience across e-commerce and video streaming/gaming-style applications, including migrating/duplicating features with feature flags and managing regression across builds. Emphasizes device/hardware constraints, strong defect evidence collection (ADB/logs/video), and proactive test-data governance to avoid real-world vendor/production impact while coordinating with offshore/onshore teams.”