Vetted Predictive Analytics Professionals

“Former CTO at Enodo who led development of programmatic parsers to extract unstructured data from real-estate financial documents (rent rolls and T12s), validating with users via prototypes before productionizing. Emphasizes accuracy-driven engineering and scalable test-suite growth based on real user samples, and has experience scoping complex product ideas (e.g., browser-based narrative editor) down to an MVP.”

“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.”

“Co-founder and employee #1 who built Reva, a vertically integrated conversational CRM for multifamily leasing and resident services, leading product, engineering, and design from inception through acquisition. Combines deep enterprise product leadership with early AI experience, including a pre-LLM chatbot that drove a 400% lift in lead conversion and prior edtech work where AI-enabled learning tools improved struggling students by a full letter grade on average.”

“Repeat early-stage startup CTO/first engineer who helped take Vettery from 0 to a $100M+ exit. Led product-oriented engineering with heavy investment in data science/ML, including a recommendations system and candidate evaluation model (90%+ predictive effectiveness), and scaled the modeling stack using parallel processing and Apache Airflow.”

“Robotics software engineer/researcher focused on perception, SLAM, and sensor fusion, with hands-on experience taking systems from simulation to embedded/real-time deployment. Led transparent-surface (glass) detection using GDNet and achieved a major real-time speedup (~7–9 FPS to ~30 FPS) while preserving >90% recall, and has built ROS-based EKF GPS-IMU fusion plus profiled/optimized Visual SLAM for performance and memory stability. Also brings production-style deployment skills via Docker/Kubernetes orchestration of ML inference services with autoscaling and model update rollouts.”

“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.”

“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.”

“Internship at ActiveVM where they tackled large-scale Spring Boot 2→3/library migrations across hundreds of downstream products by combining OpenRewrite (AST-based recipes) with an LLM/RAG-based classifier that routed risky files to human experts. Reported ~70% reduction in manual effort and 90%+ accuracy after testing across multiple branches and cutovers; also built a CTR-driven book recommendation capstone showcased at the Google office in Cambridge.”

“Data engineer in healthcare (HCA) who owned end-to-end Azure-based pipelines at very large scale (50M+ daily claims/patient records). Strong focus on reliability: schema-drift fail-fast validation, quarantine layers, and Python/SQL data quality checks that reduced issues ~25%, plus performance tuning in Databricks/PySpark and versioned serving in Synapse for downstream consumers.”

Kasireddy Kumar reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare, fraud detection, and recommender systems

Missouri, USA6y exp

“Healthcare-focused applied ML/LLM engineer who has deployed production systems including an LLM medical documentation assistant that summarizes unstructured EHR notes into physician-ready structured outputs. Experienced building secure, compliant pipelines (PHI minimization, RBAC, encryption) and scaling via Docker/Kubernetes/Azure ML, plus orchestrating ETL/ML workflows with Airflow and Kubeflow; also built an LLM-driven clinical coding assistant at Centene with measurable performance metrics.”

Chad Kilgore

Screened

Senior Technical Designer specializing in gameplay systems, tools, and VR/AAA pipelines

Bellevue, WA17y exp

“Game/technical designer with live-service ownership at Wargaming (World of Tanks) driving KPI-based monthly events and engagement loops via data-informed reward tuning. Recently shipped Batman Arkham Shadow by automating a narrative pipeline that converted script-graph dialogue into a string library, cutting weeks of work down to hours under aggressive deadlines. Experienced rapid prototyping in UE4 (including AI-driven mode logic) and cross-functional alignment practices.”

Jaideep Jain

Screened

Executive Business, Product & Engineering Leader in SaaS, Data Platforms, and IoT

33y exp

“Serial entrepreneur (13 years) currently building a consumer-focused startup with an MVP completed and initial customers acquired. Has interacted with multiple VCs and is very familiar with the VC/accelerator ecosystem, with a near-term focus on scaling customer acquisition while iterating the product.”

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.”

“Built and owned an enterprise LLM/RAG document intelligence platform for PNC Financial Services in a compliance-heavy environment, focused on grounded answers over internal finance and policy documents. Stands out for combining GenAI product delivery with production engineering discipline, delivering 60% faster document review and materially better answer quality while creating reusable FastAPI-based AI services for multiple teams.”

“Built Nexthire-AI, shipping an end-to-end LLM-powered resume–job description matching product (React + Node.js) using embeddings and retrieval to generate match scores and skill-gap recommendations. Improved post-launch engagement by making feedback cleaner and more actionable, and added production guardrails (validation, timeouts, fallbacks) to handle messy resume formats and AI API instability.”