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Vetted Model Evaluation Professionals
Pre-screened and vetted.
Senior Full-Stack AI Engineer specializing in generative AI and cloud platforms
Senior AI/ML Engineer specializing in LLM systems and FinTech platforms
Senior Full-Stack AI Engineer specializing in LLM/RAG and production ML platforms
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”
Brian Gomez
Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision
“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”
Deepak Guntreddi
Mid-level Full-Stack Developer specializing in Java/Spring Boot and React
“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Senior Research Scientist specializing in LLM verification and fraud/risk modeling
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLM apps and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Executive Robotics & AI Founder specializing in Embodied AI and Robotics Data Infrastructure
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Daming Jiang
Intern Software/AI Engineer specializing in LLM fine-tuning and agentic RAG systems
“Built and shipped an end-to-end LLM agent during an AT&T internship to automate network troubleshooting, with production-style reliability safeguards (timeouts/retries/fallbacks) and structured, state-machine orchestration; project won 3rd place in AT&T’s nationwide intern innovation challenge and was demoed to leadership. Also handled messy multi-partner data at Tencent by implementing schema validation/normalization, confidence-threshold fallbacks, and idempotent Python/ORM-based pipelines.”
Priyanka Kaswan
Senior AI Research Engineer specializing in LLM agents and large-scale ML
“AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.”
Fernando Cardenas
Executive technology leader specializing in AI products and enterprise platform modernization
“Four-time founder with hands-on experience inside angel- and venture-backed executive teams, plus accelerator mentorship experience. Brings a practical, customer-validation-driven approach to company building, with strong insight into early-stage team dynamics and investor ecosystems.”
LEQUAN WANG
Intern Applied Scientist / ML Engineer specializing in NLP and conversational AI
“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.”