Vetted OpenCV Professionals

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Backend engineer who designed and evolved an AWS-based event-processing system in Python/PostgreSQL, achieving a 60% p95 latency reduction while improving reliability during traffic spikes. Led a zero-downtime migration from a monolithic Django app to FastAPI microservices using feature flags, strong testing, and cross-team coordination, with production-grade observability (Prometheus/Grafana/CloudWatch) and security (JWT/OAuth2, RBAC, Postgres RLS).”

“Robotics/AV engineer with strong ROS2 and autonomy stack integration experience, including bringing Autoware Universe up on a real Lexus autonomous vehicle platform. Also built a hierarchical reinforcement learning proof-of-concept for Boston Dynamics Spot (navigation + manipulation) and tackled sim-to-real challenges by implementing PD torque conversion for Jetson-based hardware; improved localization accuracy via GNSS+EKF fusion with a reported 28% drift reduction.”

“LLM/agent engineer who has deployed a production internal assistant to reduce employee inquiry resolution time while maintaining regulatory compliance. Experienced with RAG, hallucination risk triage, and graph-based orchestration (LangGraph) for enterprise/banking-style workflows, emphasizing schema-validated, citation-backed, tool-constrained agent designs and tight collaboration with non-technical business/compliance stakeholders.”

“Built and deployed a production-style vision-language pipeline that generates structured medical reports from chest X-rays using BioViLT embeddings, an image-text alignment module, and BiGPT fine-tuned with LoRA, delivered via Streamlit and hosted on AWS EC2. Also collaborating experience presenting EDA findings, feature importance, and model performance to Ford managers while working with vehicle parts data at Bimcon.”

“Designed and deployed an enterprise LLM-powered clinical/pharmacy policy knowledge assistant at CVS Health, replacing manual searches across PDFs/Word/SharePoint with a HIPAA-compliant RAG system. Built end-to-end ingestion and orchestration (Airflow + Azure ML/Data Lake + vector index) with PHI masking, versioned re-embedding, and production monitoring (Prometheus/Grafana), and partnered closely with clinicians/compliance to ensure policy-grounded, auditable answers.”

“Robotics software engineer who worked on NYU’s Medi Assist robot, owning navigation sensor bring-up (LiDAR/radar/IMU) and SLAM stability, plus delivering a safety-critical braking system. Built a YOLOv8 perception pipeline on Jetson Nano and wrote STM32 firmware to actuate brakes, achieving ~50ms reaction time, and implemented diagnostics/health checks and reliable inter-board comms (ROS2 + UART with checksums/heartbeats).”

“Robotics software engineer focused on ROS2 autonomy, with hands-on work building a monocular visual odometry system on KITTI (including GPS-based scale correction and RViz trajectory visualization) and an end-to-end Gazebo simulation integrating URDF, slam_toolbox, and Nav2. Demonstrates strong practical debugging skills around TF frames, lifecycle nodes, and Gazebo plugin/version compatibility.”

“Robotics software engineer who led development of a vision-based end-effector stability/vibration analysis tool using phase-based motion magnification and frequency-domain analysis (FFT/Bode) to uncover resonances missed by motor-only diagnostics. Experienced with ROS 2 C++ perception/navigation (ArUco + PnP) and real-time industrial integration, including optimizing a 1 kHz EtherCAT/Beckhoff PLC/Modbus TCP diagnostic pipeline and designing deterministic interfaces across heterogeneous subsystems.”

“AI/LLM engineer with production experience building secure, scalable compliance-focused generative AI systems (GPT-3/4, BERT) including RAG over internal regulatory document bases. Has delivered end-to-end pipelines on AWS with PySpark/Airflow/Kubernetes/FastAPI, emphasizing privacy controls, monitoring, and iterative evaluation (A/B testing). Also partnered closely with bank compliance officers using prototypes to refine NLP summarization/classification and reduce document review time.”

“AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.”

“Candidate states they have worked on many robotics software system projects and has overcome many technical challenges, but declined to provide any project details during the screening and ended the interview early.”

“ML/LLM engineer at Charles Schwab who built a production loan-advisor chatbot integrated with internal knowledge and loan-calculator APIs, adding strict numeric validation to prevent rate hallucinations and optimizing context to control costs. Also runs ~40 Airflow DAGs orchestrating retraining/ETL/drift monitoring with an automated Snowflake→SageMaker→auto-deploy pipeline, and uses rigorous testing plus canary rollouts tied to business metrics and compliance constraints.”

“Built an AI-driven insurance policy summarization platform at Marsh, taking it end-to-end from messy PDF ingestion/OCR and custom extraction through LLM fine-tuning and AWS SageMaker deployment. Delivered measurable impact (25% reduction in manual review time, 99% uptime) and demonstrated strong production MLOps/LLMOps practices with Airflow/Step Functions orchestration, rigorous evaluation (ROUGE + human review), and continuous monitoring for drift, latency, and hallucinations.”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”