Vetted AI & Machine Learning Professionals

“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.”

“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.”

“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.”

“Built and deployed a production LLM-based RAG agent platform adopted by multiple business teams (Marketing, GTM, Recruiting, Customer Support) to automate knowledge search, Q&A, and content generation. Emphasizes production-grade reliability (grounding/validation/guardrails), rigorous evaluation/monitoring, and cost-aware scaling via model tiering, prompt/retrieval optimization, and caching using LangChain/LangGraph orchestration.”

“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.”

“AI/ML Engineer who built a production RAG-based LLM system for insurance policy documents, turning thousands of messy PDFs into a searchable index using LangChain, Azure AI Search vectors, hybrid retrieval, and FastAPI. Strong focus on evaluation (MRR/precision@k/recall@k, REGAS) and performance optimization (vLLM), with prior clinical NLP experience using BERT-based NER validated on ground-truth datasets.”

“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.”

“ML/AI practitioner with hands-on experience deploying models to production and optimizing for low-latency inference using pruning/quantization, with deployments on AWS SageMaker and Azure ML. Has orchestrated end-to-end ML pipelines with Airflow and Kubeflow (ingestion through evaluation) and emphasizes reproducibility via containerization and version-controlled artifacts, while effectively partnering with non-technical stakeholders using dashboards and business-aligned metrics.”

“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.”

“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.”

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.”

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.”

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

“AI/ML engineer at MRI Software focused on taking LLM and RAG systems from prototype to reliable production. Notable work includes an AI automation system for migrating 1200+ legacy pages with 75-80% manual effort reduction, plus enterprise document-querying and reusable Python LLM infrastructure that cut lookup time by 70% and improved team velocity by 30-40%.”

Phani K

Screened

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

Terre Haute, IN4y exp

“ML/AI engineer with hands-on experience shipping healthcare AI systems, including an oncology risk prediction platform and RAG-based clinical decision support tools. Stands out for combining clinical domain context with strong production engineering across Spark, FastAPI, AWS SageMaker, monitoring, evaluation, and safety guardrails.”

Devang Borkar

Screened

Mid-level AI Engineer specializing in LLM agents and evaluation systems

California, USA4y exp

“Built an end-to-end Python integration for an emotion-aware presentation feedback system that processed uploaded or live video and analyzed facial emotion, tone, and gestures. Also has Playwright automation experience in a loan management workflow, with emphasis on reliability, observability, security, and iterative delivery under ambiguous requirements.”

“Full-stack engineer with hands-on experience spanning React/TypeScript frontends, Cloudflare serverless RAG systems, SQL-heavy backend redesigns, and computer vision workflows. He has shipped practical automation and reliability improvements with measurable impact, including cutting a video-validation reporting process from a week to 2 days and fixing a memory-heavy shipment system before Black Friday to support 30K+ orders successfully.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

Bhagyashree Patil

Screened

Intern Applied AI Engineer specializing in LLM systems and data engineering

Los Angeles, CA1y exp

“Full-stack engineer with hands-on production experience across both traditional SaaS and LLM-powered support tooling. They owned a real-time ecommerce order tracking dashboard that improved support response times by 40%, and helped ship an AI support assistant using the OpenAI GPT API that cut ticket handling time by 30% through strong prompt design, retrieval grounding, validation, and human-in-the-loop safeguards.”

“Frontend-focused engineer building production-grade, browser-based AI products, including a live clinical decision support platform for Stanford clinicians. Stands out for combining sophisticated React/TypeScript streaming UI architecture with strong browser performance expertise, including Core Web Vitals and rendering-pipeline optimization.”