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Vetted Deep Learning Professionals
Pre-screened and vetted.
Shardul Jeurkar
Intern Applied AI/Software Engineer specializing in computer vision and full-stack platforms
“Built production LLM systems focused on reliability and safety, including a plain-English deployment tool that generates validated plans and provisions to Kubernetes while preventing unsafe actions via schema enforcement and plan/execute separation. Also created multi-LLM workflows (LangGraph) and stakeholder-friendly demos at Bosch, including a PyQt/FastAPI/CUDA app comparing SAM2 vs SAMWISE for on-device object detection with intuitive UX for business users.”
Daniel Luzzatto
Junior Machine Learning Engineer specializing in LLMs, computer vision, and robotics
“Built and deployed an agentic, multimodal LLM system that automates privacy redaction pipelines (audio/video/tabular) using LangChain orchestration and a closed-loop self-correction design. Personally implemented and performance-optimized core CV tooling (face blurring with tracking/Kalman filter) achieving >100 FPS on CPU, and validated reliability with golden-dataset benchmarking across 100+ privacy intents and measurable redaction metrics.”
Samuel Prabhakar Vara
Mid-level AI Engineer specializing in machine learning and healthcare research
“Backend engineer with end-to-end ownership of scientific and AI-powered systems, including neuron imaging pipelines at Monell Chemical Senses Center and an LLM-based structured information extraction platform for Wharton and PSG. Stands out for turning messy, compute-heavy workflows into reliable production backends with measurable impact, including saving researchers over 50 hours per week.”
Lakshmi Narayana
Mid-level Data Science AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”
Intern Machine Learning/Robotics Engineer specializing in computer vision and 3D simulation
Mid-level Machine Learning Engineer specializing in deep learning, MLOps, and real-time inference
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and recommendation systems
Intern Machine Learning Researcher specializing in LLM and GNN security
Mid-level HPC AI & Software Engineer specializing in computer vision and ML for scientific data
Mid-level AI & ML Engineer specializing in NLP, LLMs, and scalable ML systems
Mid-level AI Engineer specializing in computer vision and RAG systems
Junior Machine Learning Engineer specializing in LLMs and retrieval-augmented generation
Mid-level AI/ML Engineer specializing in fraud detection and customer lifetime value modeling
Intern AI/ML Engineer specializing in LLM systems and cloud-native microservices
Entry-Level Data Scientist specializing in Applied Analytics and Machine Learning
Junior Robotics Engineer specializing in perception, control, and mechatronic prototyping
Senior AI/ML Software Engineer specializing in LLM and RAG systems
Intern Perception/Robotics Engineer specializing in computer vision and embodied AI
Senior AI/ML Developer specializing in LLMs, RAG, and generative AI
Executive Technology Leader (CTO/CIO) specializing in cloud, AI, and enterprise transformation
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and GPU-accelerated deep learning