Vetted Feature Engineering Professionals

“Analytics professional at Santander Bank with hands-on experience building SQL and Python workflows for transaction reporting, reconciliation, and monitoring across messy multi-source financial data. They combine strong data validation and exception-handling practices with stakeholder-friendly dashboards, and also bring digital analytics experience from a Google Analytics UI optimization project focused on funnel drop-off and engagement.”

“Analytics professional at TIAA with hands-on experience combining SQL, Python, and statistical modeling to unify complex marketing, product, finance, and customer datasets. Has worked on advisor-tool adoption analysis, 10-year wealth diagnostics, forecasting, cohort analysis, and escalation-risk modeling, with findings used by marketing and contact-center stakeholders.”

“ML/AI engineer with production experience at S&P Global and Accenture, focused on regulated, enterprise-grade systems. Built end-to-end financial risk and credit default models with >90% precision and 12% fewer false positives, and is currently developing secure RAG pipelines on AWS SageMaker for enterprise insight extraction.”

“ML/AI engineer at PNC who has shipped enterprise-grade RAG and document intelligence systems for compliance and policy workflows. Stands out for combining LLM product thinking with production rigor—owning FastAPI/Kubernetes deployments, monitoring, evaluation, and human-feedback loops that drove measurable gains like 40% faster policy search and 30% faster compliance review.”

premkumar narla

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG, and enterprise ML systems

Chicago, IL5y exp

“ML/AI engineer with hands-on experience at Morgan Stanley building production fraud detection and enterprise RAG systems. Stands out for owning systems end-to-end—from experimentation and deployment to monitoring and iteration—and for delivering measurable impact, including an 18% reduction in fraud false positives, 40% lower inference latency, and internal tooling that reduced model deployment time from days to hours.”

“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.”

“Wayfair ML/AI engineer who has shipped and operated production LLM systems for both internal analytics and customer-facing assistants. Stands out for combining strong RAG/retrieval engineering with production-grade platform work—improving trust, reducing latency by ~30%, and cutting ad hoc reporting demand by ~50%.”

“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.”

“Engineer with a process-driven approach to AI-assisted software development, focused on orchestrating where AI adds value while maintaining human review and verification. Has applied this in backend work such as an S3-based invoice pipeline and used multi-agent workflows to speed up large API refactors across many endpoints.”

“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.”

Anudeep Eloori

Screened

Mid-level Software Developer specializing in full-stack enterprise applications

USA3y exp

“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”

“Full-stack engineer with experience shipping analytics dashboards and an AI-driven support assistant for a cloud analytics platform. They combine Java/Spring Boot backend work with TypeScript frontend development and showed practical knowledge of LLM production concerns like retrieval grounding, latency, caching, retries, and graceful fallbacks. Their shipped dashboard feature improved load times by 35-40% and reduced support issues tied to delayed analytics.”

“AI automations intern at a lean US-based marketing agency who works directly with founders and builds practical GTM systems end-to-end. He combines ML/LLM tooling with outbound execution, including a clustering-based recommender that improved client lead generation by 30% in two weeks and a personal cold outreach engine that achieved a 12%+ reply rate.”

“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 internal product features at Sysco's Collab Cafe across React/TypeScript frontend and Spring Boot/PostgreSQL backend, including a full project invite flow and an early AI-style project matching capability. Stands out for owning features end-to-end, improving React dashboard performance with profiling and component refactoring, and making pragmatic 0→1 tradeoffs to ship quickly.”

“Verizon engineer who productionized an LLM-based personalization capability for a customer-facing digital platform, owning the path from success metrics through scalable APIs, A/B validation, and post-launch monitoring (latency/accuracy/drift). Experienced in diagnosing and fixing real-time LLM/RAG workflow issues under peak load, and in enabling adoption via tailored technical demos/workshops and sales support materials.”

“Robotics software engineer focused on ROS 2 localization/SLAM: built a particle-filter (Monte Carlo) localization system in Python with likelihood-field modeling to handle noisy LiDAR and dynamic environments. Strong in debugging ROS 2 integration issues (tf2 frame sync, DDS/QoS message reliability) and in profiling/optimizing pipelines to reach real-time performance (~10 Hz) using precomputation and KD-trees.”

“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”

“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.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“AI/ML engineer with healthcare domain depth who led a HIPAA-compliant, production LLM system at McKesson to automate clinical document understanding—extracting entities, summarizing provider notes, and supporting authorization decisions. Hands-on across Spark/Python ETL, Hugging Face + LoRA/QLoRA fine-tuning, RAG, and cloud-native MLOps (Airflow/Kubernetes/Step Functions, MLflow, blue-green on EKS/GKE), with explicit work on PHI handling and hallucination reduction.”

“Built and deployed a GPT-based RAG enterprise search system for healthcare clinicians, emphasizing low-latency performance and reduced hallucinations while maintaining end-to-end HIPAA compliance. Demonstrates deep applied experience with PHI-safe data governance (detection/redaction/de-identification), secure Azure ML deployment patterns, and orchestration of production LLM workflows using LangChain and Airflow.”

“Accenture data/ML practitioner who deployed a retail churn prediction and BERT-based sentiment analysis system to production, integrating behavioral + feedback data and operationalizing it with ETL automation, orchestration, and CI/CD. Experienced managing 2TB+ multi-source data, monitoring drift in Databricks, and translating results into Power BI dashboards for marketing teams (including K-means customer segmentation).”

“ML/LLM engineer who has shipped production RAG systems (LangChain + HF Transformers + FAISS) with hybrid retrieval and cross-encoder re-ranking, deployed via FastAPI/Docker/Kubernetes and monitored with MLflow. Also partnered with wealth advisors at Edward Jones to deliver a client retention model with SHAP-driven explanations and a dashboard that improved trust, adoption, and reduced high-value client churn.”