Vetted Natural Language Processing Professionals

Pre-screened and vetted.

VJ

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

Chicago, IL6y exp
AbbVieGovernors State University
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AK

Junior Data Engineer specializing in cloud data platforms and MLOps

Indianapolis, IN1y exp
Eli LillyNortheastern University
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SC

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

4y exp
CVS HealthUniversity of Central Missouri
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SB

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA4y exp
CVS HealthUniversity of Houston-Clear Lake
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NC

Mid-level Data Analyst specializing in business intelligence and predictive analytics

Fort Wayne, IN4y exp
DeloitteIndiana Tech
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TK

Mid-level AI/ML Engineer specializing in production ML and FinTech

Iowa, USA4y exp
IntuitUniversity of Dayton
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SA

Senior Machine Learning Engineer specializing in Generative AI and LLM systems

Brooklyn, NY6y exp
Codex InnovationLahore University of Management Sciences
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VP

Mid AI/ML Engineer specializing in MLOps, deep learning, and cloud ML systems

Colorado, USA3y exp
BNY MellonUniversity of Colorado Boulder
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NK

Senior Data Scientist and AI Engineer specializing in NLP, LLMs, and MLOps

Milwaukee, WI10y exp
CaterpillarWest Virginia University
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KR

Mid-level AI/ML Engineer specializing in Financial Services

Atlanta, GA4y exp
American ExpressUniversity at Buffalo
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RA

Senior AI/ML Engineer specializing in LLM, NLP, and production ML systems

Plano, TX11y exp
CignaUniversity of North Texas
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KS

Entry Data Scientist specializing in ML, NLP, and GenAI

Hyderabad, India1y exp
KofluenceRowan University
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SS

Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems

6y exp
Bank of America
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SD

Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms

Westfield Center, OH7y exp
Westfield Insurance
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AM

Principal Automation Architect specializing in cloud DevOps, microservices, and MLOps

Spring, TX16y exp
SparkSoft
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VT

Venkatesh ThimmaDhinakaran

Screened ReferencesModerate rec.

Director-level Software Engineering Manager specializing in full-stack SaaS and communications platforms

Princeton, NJ12y exp
Hyper-ReachRochester Institute of Technology

Hands-on Engineering Manager at Hyper-Reach modernizing a mission-critical emergency alerts platform used by county emergency directors and first responders during live disasters. He led a phased migration from a 25-year-old JSP monolith to React, shipped in 5 months instead of an estimated 1.5 years, with zero regression incidents and clear customer and sales impact.

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Sanjay Mamani - Executive Technology Leader specializing in enterprise data, AI, and cloud analytics in Mooresville, USA

Sanjay Mamani

Screened ReferencesStrong rec.

Executive Technology Leader specializing in enterprise data, AI, and cloud analytics

Mooresville, USA26y exp
BridgetreeUniversity of North Carolina at Greensboro

25-year builder/operator who has scaled others' visions and led VC-backed startup incubation work (Saltmines). Built Bridgetree’s AI CoE from 0 to 1 and cites $20M+ measurable customer impact, with experience leading 110-person cross-disciplinary teams. Exploring a new venture idea (gotAgentic.ai) focused on agentic AI solutions such as AI-ready data prep, agentic SDLC teams, and front-office automation (scheduling/invoicing).

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CH

Mid-level AI/ML Engineer specializing in healthcare, risk modeling, and MLOps

Milwaukee, WI3y exp
UnitedHealth GroupUniversity of Wisconsin–Milwaukee

Robotics software engineer who built a ROS Noetic-based perception-to-control stack for a pick-and-place robotic arm, integrating OpenCV/TensorFlow vision with motion planning and PID tuning. Demonstrated strong real-time debugging skills (rosbag, queue/latency fixes) and experience deploying reproducible robotics environments with Gazebo simulation, Docker, and GitLab CI.

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UW

Ujwal Waghray

Screened

Mid-level Robotics & Software Engineer specializing in ROS 2 autonomy and ML

Buffalo, NY4y exp
WINGS Lab, SUNY BuffaloSUNY

Master’s-level IoT course project that the candidate helped evolve into a research lab effort by “ROSifying” a soil-fertility detection rover (autonomous navigation within a GPS geofence, sensor fusion, and rover-to-base-station telemetry via NRF24 to a Raspberry Pi dashboard). Also built a ROS/Gazebo vision-based teleoperation system using a SigLIP hand-gesture model mapped to geometry_msgs/Twist, and improved stability by instrumenting and filtering a latency-prone perception-to-control pipeline.

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SP

Junior AI Engineer specializing in RAG pipelines and agentic AI systems

San Francisco, CA2y exp
Avenio CorporationGeorge Washington University

Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.

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PK

Mid-level AI/ML Engineer specializing in NLP, GenAI, and MLOps in healthcare and finance

USA5y exp
CVS HealthUniversity of Houston

AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.

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VN

Mid-level Software Engineer specializing in ML, LLM apps, and cloud data systems

Tracy, California4y exp
GeneaUC Santa Cruz

Built a production SQL chatbot for access-log analytics that replaced manual custom report requests with natural-language querying, using LangGraph and a ChromaDB-backed RAG pipeline for grounded, consistent answers. Implemented a privacy-preserving design where the LLM never sees raw customer data (only query metadata) and has experience building multi-agent/tool-calling systems with LangGraph (DeepAgents), including solving sub-agent communication drift via self-reflection.

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