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Vetted AI & Machine Learning Professionals in the San Diego Metro
Pre-screened and vetted in the San Diego Metro.
Junior Machine Learning Engineer specializing in fraud detection and healthcare ML
Senior Applied AI Engineer specializing in enterprise AI and ML platforms
Gagan Mundada
Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks
“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”
Zufeshan Imran
Senior Machine Learning Engineer specializing in LLMs, RAG, and computer vision
“Built an "AskMyVideo" system that turns YouTube videos into queryable knowledge graphs by transcribing audio (Whisper), chunking and embedding content, and enabling traceable answers back to exact timestamps. Strong in entity resolution (rules + fuzzy matching + TF-IDF/cosine with PR-curve thresholding) and modern retrieval stacks (FAISS, hybrid dense/sparse, domain fine-tuning with ~12% precision gain), with a production mindset using Airflow/Prefect, Docker/FastAPI, and LangSmith/Prometheus/Grafana observability.”
Alexander Taylor
Principal Automation & Robotics Engineer specializing in lab automation deployments
“Lab automation engineer building an automated weighing robot system for Lilly’s analytical chemistry group, integrating a 6-axis Mecademic robot, Mettler Toledo balance, and vision/barcode verification with safety protocols for live lab operation. Experienced in production Python for instrument control, data processing, and CV/ML, including authenticated Data Lake API integrations with fault handling. Also improved automated sample storage throughput 2–3x via pick-order optimization and partners closely with scientists to deliver MVP-to-production experimental automation workflows.”
Junior AI/Software Engineer specializing in LLMs, NLP, and cloud infrastructure
Karan Variyambat
Mid-level Machine Learning Engineer/Researcher specializing in computer vision and multimodal AI
“Developed a production wildfire smoke detection system where smoke is visually subtle and easily confused with fog/clouds; addressed this with a hybrid CNN+LSTM+ViT model and multimodal weather features to reduce false positives. Experienced running scalable, reproducible ML pipelines on shared GPU infrastructure using Slurm and Kubernetes-style batch jobs with checkpointing, retries, and rigorous error analysis.”
Ishan Kumar Anand
Junior AI/ML Engineer specializing in multimodal generative models and NLP
“AI/ML engineer who has built a production text-to-image generation system in PyTorch with an AWS-backed inference setup, focusing on GPU-efficient training and embedding-space architectural choices inspired by recent research (e.g., Meta VL-JEPA). Uses both metric-based evaluation (FID) and human testing to validate real-world visual quality, and can translate technical concepts for non-technical stakeholders.”
Senior Software Engineer (ML) specializing in LLM systems and compliance platforms
Junior AI Engineer & Data Scientist specializing in GenAI and Computer Vision
Alex Nguyen
Junior Applied AI Engineer specializing in LLMs, RAG, and agentic systems
“Co-founded a healthcare AI startup building and deploying software directly with end users, emphasizing rapid shipping, deep user interviews, and workflow-first adoption. Has hands-on production deployment experience on AWS (including diagnosing a silent AWS App Runner failure caused by an ARM vs amd64 Docker build mismatch) and is motivated by customer-facing, travel-heavy roles to keep engineering tightly connected to real-world usage.”
Sampada shelke
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and applied research
“New grad SDE (AI/ML) who built and deployed an LLM-based chatbot framework used across technology, military, and banking contexts, focusing on model selection tradeoffs (latency vs accuracy) through prototyping and benchmarking. Also built a multi-agent "eaterybot" using PyAutoGen/AutoGen with a manager agent orchestrating specialized agents, and emphasizes rigorous testing with adversarial/edge-case datasets and hallucination checks.”
Intern AI Engineer and Data Scientist specializing in NLP, LLMs, and applied ML
Intern Machine Learning Engineer specializing in LLMs, generative AI, and reinforcement learning
Mid-level Machine Learning Engineer specializing in deep learning and applied research
Supriya Thorat
Mid-level Software Engineer specializing in full-stack web and cloud automation
“Full-stack TypeScript/Angular/Node engineer who owned a production healthcare application for a pharmaceutical client, supporting 100K+ monthly users across 10+ countries. Strong focus on maintainability and quality (reusable localized component library, ~90% unit test coverage, SonarQube in CI/CD) plus performance work (reported 15% client-side latency reduction and up to 50% backend latency reduction) while migrating legacy mobile code with strict backward compatibility.”
Mid-Level AI Engineer specializing in LLM systems, GPU optimization, and multi-agent orchestration
Intern Machine Learning Engineer specializing in NLP, MLOps, and cloud AI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and cloud AI infrastructure
Mid-level AI/ML Engineer specializing in LLM agents, search/recommendation, and MLOps