Vetted TensorFlow Professionals

“Embedded/robotics software engineer with Hyundai Motors experience who owned an AI-driven perception validation pipeline using a Transformer-based approach to generate stable synthetic in-cabin audio for autonomy/ASR testing, cutting downstream testing time by 50%+. Has hands-on ROS integration (IMU sensor streaming, inference, control nodes), MQTT-based distributed messaging, and cloud/container deployment experience (Docker, Node/Express, AWS, CI/CD).”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

“Robotics/ML engineer with internship experience at Valeo building a deep-learning prototype to replace parts of a legacy SLAM backend for autonomous parking, focused on making models run reliably in real time on embedded hardware (quantization/distillation + TensorRT). Also brings strong MLOps/deployment experience (Docker, Kubernetes on AWS EKS, CI via GitHub Actions) and has supported patent filing by explaining the technical approach to legal stakeholders.”

“Meta engineer who improved a critical revenue estimation dataset pipeline that was arriving ~6 days late—diagnosed via raw logs/lineage, redesigned legacy scans to only process the needed window, and shipped validation plus freshness/lag dashboards. Delivered ~50% latency reduction (to ~3 days) and regained adoption by running old/new pipelines in parallel with gated cutover and evidence-based customer communication. Applies incident-response rigor to real-time LLM/agentic workflow debugging and regularly runs developer demos/workshops.”

“LLM/Conversational AI engineer who built a production multi-turn dialogue system using LoRA fine-tuning on LLaMA, cutting training compute/memory by 90%+ while maintaining low-latency inference via quantization and streaming generation. Experienced in orchestrating end-to-end ML workflows with Prefect/Airflow/Kubeflow (including hyperparameter sweeps and W&B tracking) and improving agent reliability through benchmark-driven testing, shadow-mode rollouts, and stakeholder-informed guardrails.”

“Internship at BlueFoxLabs building and deploying an AI/ML RAG system for a biopharma client on top of LibreChat, including an AWS Textract ingestion pipeline and PGVector retrieval deployed to AWS EKS. Demonstrated production-minded scalability work by moving from a vertically scaled EC2 setup to a horizontally scaling Kubernetes/EKS deployment, using CI/CD to safely incorporate requirement changes like tabular document data.”

“Backend/data engineer from Oscar Health specializing in healthcare claims systems on AWS. Built HIPAA-compliant real-time services (FastAPI/Postgres/Kafka on EKS) and serverless ingestion pipelines, and led modernization of a legacy SAS claims pricing system to Python/Spark with rigorous parity validation. Demonstrated measurable impact with high uptime/low latency services and major Snowflake performance and cost reductions.”

“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”

“DevOps/SRE focused on cloud-based distributed systems, with strong hands-on Kubernetes production experience (microservices deployments, Helm, probes, resource tuning, CI/CD and Docker build standardization). Demonstrated end-to-end troubleshooting across application, infrastructure, and networking layers—e.g., isolating degraded storage via node disk I/O metrics and restoring performance by draining the node and replacing the volume. Builds Python automation for operational reliability, including scheduled Kubernetes secrets rotation integrated with an external secret manager.”

“Built and shipped a real-time AI phone agent for small businesses that handles bookings/FAQs/messages using streaming ASR, an LLM with tool-calling, and TTS; deployed to production for multiple paying customers. Demonstrates strong applied LLM reliability practices (tool-first grounding, retrieval, hard-negative testing, and production monitoring) and experience orchestrating multi-step AI workflows with Airflow, Prefect, and AWS Step Functions.”

“Robotics/embedded engineer with hands-on experience building real-time control systems on RP2040 (hydroponics automation, 1-DOF helicopter stabilization) and full ROS 2 navigation stacks in simulation (URDF, TF, PID, A* in RViz/Gazebo). Demonstrates strong low-level protocol work (timing-sensitive one-wire in C) and rigorous debugging across hardware and software using UART instrumentation and oscilloscope verification, plus reproducible workflows with Docker and CI/CD (GitHub Actions/GitLab, incl. Sandia National Labs).”

“Amazon Robotics engineer who deployed and scaled the Lumos camera-based package scanning work cell across EU sort centers (100+ work cells in 5+ sites), enabling remote launches via detailed runbooks and troubleshooting. Strong in AWS IoT/edge systems, with hands-on incident recovery (restored 34 down work cells) and secure multi-compute certificate provisioning using IoT Jobs, ACM/CA, and custom roles; delivered ~75% per-cell cost reduction vs Cognex-based approach.”

“Technology/architecture leader with Accenture experience delivering data- and AI/ML-driven products, including a legal contract search solution and customer sales analytics for AWS. Known for scaling distributed teams (onshore/offshore), making pragmatic architecture decisions, and solving hard data problems (proprietary sources, data quality) while implementing scalable integrations like Redshift-to-Salesforce via parallelized pipelines.”

“Robotics engineer who helped bootstrap Meta’s humanoid robotics effort, building simulation training and deployment infrastructure for vision-language-action (VLA) models. Evaluated multiple physics backends (Bullet, MuJoCo, Isaac, internal) to minimize sim-to-real gap and addressed control-loop frequency mismatches via sequence optimization/MPC-like approaches and trajectory-output modifications. Published research that contributed a new addition to ROS 2 and has built ROS2 node stacks spanning control, perception, teleop, tactile sensing, and imaging.”

“Backend/AI engineer who built a production GPU-backed real-time inference API at Nvidia and debugged burst-induced tail latency, cutting P95 by ~29% through dynamic batching and backpressure. Also shipped an end-to-end RAG + agentic operational diagnostics assistant with strict tool controls, evidence citation, confidence gating, and strong production guardrails, plus demonstrated hands-on Postgres optimization (900ms to 40–60ms).”