Vetted Data Preprocessing Professionals

“Cheryl Fernandez is a frontend/platform engineering leader who has built complex real-time products in highly regulated healthcare and enterprise investigation environments. She combines deep React/TypeScript and streaming architecture expertise with cross-functional ownership across APIs, infrastructure, device behavior, and product strategy, including modernizing FDA-audited remote patient monitoring systems and adding AI-assisted workflows at EY.”

“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”

“Built and deployed an AI Copilot for Healthful Telehealth that helps dietitians generate personalized meal plans using patient data and real-time clinical context. Stands out for owning the full lifecycle—from workflow discovery and ETL/RAG architecture to production incident response and post-launch stabilization—while delivering roughly 30% gains in retrieval accuracy and latency.”

Bibhash Biswas

Screened ReferencesStrong rec.

Executive technology leader specializing in AI/ML, SaaS, and cloud architecture

Los Angeles, CA25y exp

“Hands-on CTO and player-coach engineering leader from MindShow who led a 10-person team, transformed a services business into a licensable product sold to Fortune 500 customers, and stayed deep in architecture, AI, and UI work. Particularly notable for combining microservices/system design leadership with practical AI product delivery in speech and media workflows, including a phoneme-generation system the candidate says achieved 99.9%+ accuracy.”

“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”

“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”

Dylan Zhu

Screened

Mid-level Machine Learning Engineer specializing in computer vision and generative AI

Hoboken, NJ7y exp

“Built and deployed an LLM/RAG system that uses differential privacy and distributional similarity checks to transform private data into a non-sensitive knowledge base while preserving utility. Also has experience demonstrating adversarial ML concepts (FGSM) to non-technical audiences by focusing on observable model behavior rather than implementation details.”

shivapriya pillalamarri

Screened

Mid-level AI/ML Engineer specializing in financial analytics and production ML systems

Boston, MA4y exp

“Analytics candidate with experience in financial transaction and fraud detection projects, combining SQL data preparation, Python-based automation, and dashboarding. They have owned projects from stakeholder alignment and metric definition through rollout, with emphasis on reducing false positives, improving operational efficiency, and making analytics outputs easy for business teams to adopt.”

“JavaScript/React/TypeScript engineer with hands-on open-source experience improving a hooks utility library—fixed a reported async race condition that reduced unexpected re-renders and added a debounced callback hook that became widely used. Brings a production-minded approach to performance and abstractions (APM/metrics-driven, DB/caching focus) with strong testing, documentation, and community support practices.”

“Frontend-leaning full-stack engineer who built internal product capabilities at Mercedes-Benz R&D, including a vehicle exploration platform, test drive booking flow, and a 0→1 vehicle comparison feature. Stands out for combining strong React architecture and performance optimization with practical backend/API ownership in Node/Express and MongoDB.”

“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”

Vineet Jujjavarapu

Screened

Mid-level Software Engineer specializing in cloud-native data platforms

College Park, MD3y exp

“Software engineer with hands-on experience using AI coding assistants and LangChain-based agent workflows in RAG/LLM projects. Stands out for combining practical multi-agent experimentation with strong grounding in system design, distributed systems, and production-minded validation of AI-generated outputs.”

“AI/full-stack engineer who has built a production-style LLM knowledge assistant from scratch, combining FastAPI, LangChain, FAISS, semantic retrieval, and a user-facing chat interface. Stands out for owning both the technical architecture and the product usability layer, including latency optimization, prompt refinement, and source-backed responses to improve trust for non-technical users.”

“Built a production AI-powered customer support Q&A system using an internal knowledge base to reduce repetitive ticket work and improve customer satisfaction, with an emphasis on source-backed answers and expert oversight. Also has experience defining deployment services in a microservices architecture and integrating large-scale APIs (including work connected to US HHS/COVID-19).”