Vetted Data Ingestion Professionals

“Built and deployed an Azure-based, fine-tuned CLIP visual retrieval system at Staples for a ~300k-item product catalog, improving edge-case recall by 12% by engineering a custom delta-similarity/dynamic-margin loss. Also has robotics experience using ROS2 for sensor/compute orchestration, including GPS-time-synchronized sensor triggering for robot swarms and latency-bounded optical-flow benchmarking for edge deployment.”

“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.”

“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.”

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“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).”

“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”

“Backend engineer with recent Barclays experience building a Python asyncio + Kafka risk reporting service for trading desks, including a major refactor from blocking batch processing to event-driven incremental pipelines to restore intraday/EOD performance. Also shipped an applied AI feature using OpenAI fine-tuning to classify risk-breach severity and generate trader/risk-manager summaries with robust retry/fallback handling, plus demonstrated strong database/query optimization (triggers, materialized views, partial indexes) in a risk-limits/breaches domain.”

“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.”

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

Cristian Vega

Screened

Senior AI/ML Engineer specializing in Generative AI and RAG

California, null9y exp

“ML/NLP practitioner at Morf Health focused on unifying fragmented healthcare data by linking structured patient/encounter records with unstructured clinical notes. Has hands-on experience with transformer embeddings, vector databases, and domain fine-tuning, plus rigorous evaluation (precision/recall) and human-in-the-loop validation with clinical SMEs to make pipelines production-grade.”

Prithviraju Venkataraman

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and Computer Vision

Long Beach, CA5y exp

“Built and deployed a production LLM-powered text extraction/classification system that converts messy unstructured reports into searchable insights, running on AWS SageMaker with automated retraining and monitoring. Strong in orchestration (Step Functions/Kubernetes/Airflow patterns) and reliability practices (gold datasets, prompt/tool unit tests, shadow/canary/A-B testing, guardrails/rollback), and has experience translating non-technical stakeholder needs into an NLP workflow plus dashboard.”

Priyanshi Sharma

Screened

Mid-level Software Engineer specializing in FinTech full-stack and backend systems

Parsippany, NJ4y exp

“Built and productionized a GenAI prompt-engineering solution to retrieve prevailing wages based on job/location selections, emphasizing accuracy through stricter prompt templates and validation. Hands-on in real-time production debugging using Splunk (callback tracing, verbose logging, header inspection) and experienced running developer-facing demos/workshops that helped drive marketplace API adoption.”

Pavan Punna

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and healthcare-fintech AI

Dallas, TX5y exp

“Built and owned a production GPT-4 RAG assistant for clinical and enterprise query resolution, taking it from initial experiment to deployment, monitoring, and iterative improvement. Their work cut resolution time from 45 minutes to under 2 minutes, achieved roughly 95% accuracy, and scaled to thousands of additional monthly queries while emphasizing safety and trust in a sensitive clinical domain.”

ASHISH DONDAPATI

Screened

Mid-level AI/ML Engineer specializing in Generative AI for Financial Services

San Francisco, USA6y exp

“ML/AI engineer with strong financial-services domain experience who has built production systems spanning trade anomaly detection, investment-research RAG, and agentic LLM workflows. Particularly compelling for teams needing someone who can take ML/GenAI from prototype to monitored production while balancing compliance, latency, cost, and reliability.”

“Software developer with a one-year Philips co-op who has already applied AI-assisted coding in production, not just side projects. Stands out for using multi-agent development setups with task-specific sub-agents and a clear human-led orchestration philosophy focused on context, quality control, and security.”

“LLM/agentic systems practitioner who specializes in moving demo-only assistants into reliable, observable, cost-controlled production services. Strong in real-time diagnosis of complex agent workflows (including tracing, loop detection, and guardrails) and in customer-facing enablement—running workshops, building tailored PoCs, and partnering with sales to close deals by proving reliability in high-risk pilots.”

“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.”

“Backend-focused engineer at Wells Fargo owning production payments features end-to-end, including Spring Boot REST services, CI/CD + containerized AWS deployments, and CloudWatch-based observability. Has hands-on experience stabilizing high-traffic transaction workflows and building reliable ingestion/integration flows using idempotency, retries/backoff, and reconciliation.”

“Master’s CS candidate with backend internship experience modernizing live operational workflows at NatWest/NetWess, focusing on reliability improvements, safer CI/CD deployments, and incremental refactors using feature flags and rollback paths. Built FastAPI-based APIs with strong security patterns (JWT + 2FA/TOTP, centralized authorization, RLS) and demonstrated attention to edge cases like idempotency and data consistency in a Netflix-clone project.”

“Backend/data engineer with hands-on production experience across Python REST APIs and PostgreSQL, plus AWS containerized deployments using CloudFormation, Jenkins CI/CD, and CloudWatch monitoring/autoscaling. Has built data validation/ETL-style workflows with schema/version checks and targeted reprocessing, modernized legacy batch processing into Java services with phased parallel migrations, and delivered measurable SQL performance gains (~50% query runtime reduction).”

“Software engineer with a CS/Computer Engineering background who has worked on ML/NLP (Hugging Face, clinical NLP, text generation and structured extraction) and has a school robotics project integrating a trained ML model with microprocessor-controlled hardware to drive motor movement and writing. Currently focused on building and deploying applications and ML models to AWS/Azure using Docker, Kubernetes, and CI/CD; targeting ~$150K compensation.”

“Open-source JavaScript contributor focused on data visualization, extending Chart.js/React with custom plugins for real-time streaming dashboards. Designed an end-to-end telemetry pipeline using Apache Kafka and Azure Cosmos DB, optimizing partitioning, batching, caching, and client throttling to keep latency low and support thousands of concurrent users. Demonstrates strong ownership in fast-changing environments, including building full-stack AI applications and ingestion/ETL pipelines at Robotics Technologies LLC.”