Vetted pandas Professionals

“Built an LLM-powered academic research assistant for a professor (LangChain + OpenAI + arXiv) focused on synthesizing papers quickly, with emphasis on reliability (ReAct prompting, citation verification) and cost control (caching). Has production MLOps/orchestration experience at Cisco and HCL Tech using Kubernetes, plus MLflow and GitHub Actions for lifecycle management and CI/CD.”

“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”

“Built a production industrial anomaly detection system for a laminator using only limited runtime logs (time/pressure/temperature) and scarce abnormal examples. Addressed inconsistent manual labeling across customers by creating an operator feedback loop for remarking predictions and retraining customized models, and communicated results to a non-technical company liaison using clear tables, trend plots, and interactive demos.”

“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”

“Recent J&J intern who built a conversational RAG agent and led a shift from a monolithic model to a modular RAG workflow, cutting response time from several days to under a second by tackling data fragmentation, context retention, and embedding/latency optimization. Also worked on a large (7B-parameter) multimodal VQA pipeline for healthcare research and stays current via NeurIPS/ICLR and open-source contributions.”

“Robotics engineer with hands-on experience scaling a multi-vendor heterogeneous warehouse robot fleet, building a distributed “traffic manager” for collision avoidance and real-time rerouting using CBS/MAPF and DCOP-style negotiation. Strong real-time/safety-critical systems background (RTOS, deterministic lock-free multithreading) plus modern perception and simulation tooling (CNN-LSTM/transformers, CARLA/Isaac Sim, VIO/GTSAM, camera-IMU calibration). Startup-oriented and comfortable moving quickly from prototype to production.”

“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”

“AI/ML engineer who has deployed transformer-based NLP systems to production via Python REST APIs and Kubernetes on AWS/Azure, with a strong focus on latency optimization (p95), reliability, and scalable orchestration. Demonstrates pragmatic model tradeoff decision-making and strong stakeholder collaboration—improving adoption by making outputs more actionable with summaries, extracted fields, and confidence indicators.”

“Data/ML practitioner with experience at Airtel and Lincoln Financial delivering measurable business outcomes: improved retention 15% via NLP sentiment analysis and cut response time ~25% using sentence-BERT + FAISS semantic linking. Strong in data quality/identity resolution (SQL + fuzzy matching) and in building production-grade Python workflows orchestrated with Airflow/AWS Glue, including validation and dashboard integration in Power BI.”

“Built and deployed an end-to-end product that brings a research-paper approach into production for large-scale time-series clustering, with attention to partitioning, latency, and scalability. Also designed a Python-based backend validation service (comparing outputs to database ground truths) and handled production reliability issues by reproducing dataset-specific crashes and hardening corner-case behavior with client-friendly errors.”

“Data engineer with healthcare domain experience who has owned end-to-end pipelines and APIs at UnitedHealth Group, processing ~8M records per batch. Strong focus on data quality (multi-layer validation), reliability (monitoring/logging, retries/idempotency), and performance (Spark/SQL tuning, caching), with experience standing up early-stage systems using Python, Docker, and CI/CD.”

“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”

Chandan Chalumuri

Screened

Mid-level Data Scientist specializing in ML, NLP, and Generative AI

Tempe, AZ4y exp

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

Manpreet Kour

Screened

Senior Data Scientist specializing in Generative AI and NLP

Seattle, USA6y exp

“ML/NLP engineer with recent Scotiabank experience building production-grade indexing automation over large-scale emails and customer databases, combining LLM fine-tuning (Mistral, XLM-R) with fuzzy matching to exceed 95% accuracy under strict banking constraints. Also built a RAG-based chat agent using Gecko embeddings, Vertex AI Search, Gemini, and cross-encoder reranking, and delivered a text-to-SQL chatbot at SOTI through iterative fine-tuning and benchmark-driven experimentation.”

Vedang Jadhav

Screened

Mid-Level Software Engineer specializing in cloud-native microservices on AWS

New York City, NY5y exp

“Backend engineer with experience across healthcare and fintech platforms (Anthem, Citia) building high-throughput Python microservices with strong compliance/security focus (HIPAA, tenant isolation). Has integrated ML workflows into production systems (ResNet embedding-based image similarity) using async pipelines (Celery/Redis) and AWS (Lambda/S3/ECS), delivering measurable performance and fraud/content-integrity improvements at scale.”

vineetha Pulipati

Screened

Mid-level Software Engineer specializing in backend microservices and cloud data pipelines

MO, USA4y exp

“Backend engineer with Morgan Stanley experience building and owning an end-to-end Python FastAPI microservice for high-volume market data used by trading and risk systems. Strong in performance tuning and reliability (PySpark, Redis caching, async APIs), real-time streaming with Kafka, and production operations (Docker/Kubernetes, GitOps-style CI/CD, monitoring). Has led cloud/on-prem migration work across AWS and Azure, including fixing Azure Synapse performance issues via query and pipeline redesign.”

Sharanya Rao

Screened

Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare

Remote, USA3y exp

“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”

Erik Moyer

Screened

Director-level Data Science & Analytics Leader specializing in cloud data platforms and AI/ML

Dallas, TX13y exp

“Candidate states they are very familiar with the venture capital/studio/accelerator landscape and expresses strong willingness to pursue entrepreneurship "at all costs," but did not provide details on a current startup, business plan, fundraising, or prior accelerator/VC involvement during the interview.”

srilekha pothula

Screened

Mid-level Data Engineer specializing in cloud data pipelines for healthcare and financial services

Bloomfield, CT4y exp

“Data engineer with ~4 years of experience (Cigna) building and operating Azure Data Factory pipelines for healthcare claims/member/provider data at 2–3M records/day. Emphasizes reliability and downstream safety via schema/data-quality validation, quarantine workflows, idempotent processing, and backfills; also improved runtime ~20% through SQL optimization and served curated datasets through versioned views and well-documented, analyst-friendly interfaces.”

Asvad Shaik

Screened

Mid-level AI/ML Engineer specializing in Generative AI, RAG, and NLP

Dallas, TX5y exp

“Backend engineer who built and migrated a large-scale document intelligence platform used by legal, healthcare, and insurance clients, processing millions of pages. Experienced moving from a monolithic, LLM-heavy approach to a modular FastAPI service architecture with ML classification + RAG, strong validation/auditability, and enterprise security (JWT/OAuth, RBAC, PostgreSQL RLS) with zero-downtime incremental rollouts.”

Abhishek Saraswat

Screened

Mid-level Software Engineer specializing in Java backend microservices

Arlington, TX4y exp

“Backend/distributed-systems engineer focused on automation and near-real-time processing, building Java/Spring Boot microservices with Kafka, PostgreSQL, and AWS. Strong in scaling and reliability work—debugging tricky asynchronous messaging issues (delays, duplicates, out-of-order events) and improving resilience/observability with retries, fallbacks, logging, and monitoring. No production ROS/ROS2 experience yet, but has studied core ROS concepts and draws clear parallels to event-driven architectures.”

“Data-focused engineer who has built near real-time trending news sentiment pipelines end-to-end (API/web ingestion, validation, transformations, and dashboard serving) and implemented reliability patterns like retries with exponential backoff and backfills. Also shipped Java/Spring Boot REST APIs backed by SQL with indexing/pagination, and stood up an early-stage QR-based attendance MVP using Firebase with iterative hardening via logging and validation.”

“Data Engineer (TCS) who owned an end-to-end CRM analytics pipeline for Bayer’s eSalesWeb integration, ingesting from Salesforce APIs/databases/S3 and serving analytics-ready datasets via PostgreSQL/S3 for Tableau. Drove measurable outcomes: ~60% reduction in manual data-quality effort, ~30% lower latency through SQL optimization, and ~35% improved stability via monitoring, retries, and idempotent processing.”