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Vetted Transformers Professionals
Pre-screened and vetted.
Intern Data Scientist specializing in NLP and Large Language Models
Mid-level AI/ML Data Engineer specializing in analytics, ML pipelines, and LLM applications
Mid-Level Software Engineer specializing in cloud-native distributed systems
Senior Data Scientist specializing in Generative AI, NLP, and MLOps
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML systems
Executive CTO/VP Engineering specializing in high-performance AI, data systems, and distributed infrastructure
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
Rubeena Riyas
Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms
“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”
Manish Mallikarjuna Devatha
Principal Applied Scientist specializing in ML systems and Generative AI
“Built and owned an end-to-end agentic RAG chatbot platform for Baptist Health that helped clinicians access policy and clinical documents faster, reducing manual lookup by 80% and delivering about $2M in annual savings. Brings strong healthcare GenAI production experience, including HIPAA-aligned governance, PHI redaction, observability, evaluation, and scalable Python/Kubernetes deployment practices.”
Siliang Zhang
Intern Machine Learning Engineer specializing in LLMs, RAG, and vision-language systems
“Robotics ML/software engineer focused on Vision-Language-Action control for 7-DoF robots, replacing tokenized action decoding with continuous regression heads (including a logit-weighted expectation approach) to improve stability and real-time behavior. Strong in ROS1/ROS2 systems integration and debugging closed-loop manipulation issues via latency instrumentation, QoS-aware distributed messaging, and sim-to-real validation using Gazebo/Unity, Docker, and CI pipelines.”
“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”
Nicolas Collins
Senior Full-Stack Engineer specializing in AI and cloud-native applications
“Built and shipped a production LLM-powered internal developer tool that accelerated code reviews by about 30% while maintaining reliability through modular orchestration, validation, and monitoring. Demonstrates strong practical depth in agent architecture, backend workflow orchestration, and observability for non-deterministic AI systems, with concrete examples of reducing agent errors by 60%.”
Staff Machine Learning Engineer specializing in LLM agents and ML systems
Intern software engineer specializing in AI, cloud, and full-stack systems
Senior Full-Stack Engineer specializing in Python, cloud-native systems, and LLM applications
Intern Software Engineer specializing in cloud infrastructure and distributed systems
Junior Machine Learning Engineer specializing in generative modeling and computer vision
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
Intern AI/ML Engineer specializing in data science, NLP, and reinforcement learning
Intern AI/Data Science Engineer specializing in LLM agents, data engineering, and predictive analytics
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps