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Vetted Hugging Face Transformers Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in Python, distributed systems, and AI backend services
Mid-Level Software Development Engineer specializing in AWS serverless and ML/GenAI
Mid-level Machine Learning Engineer specializing in generative AI, NLP, and MLOps
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems
Kenil Tanna
Staff-level Machine Learning Engineer specializing in LLMs and MLOps for Financial Services
“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”
Nishitha Thummala
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference
“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”
Krishna Reddy
Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants
“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
Senior Full-Stack Python Developer specializing in cloud-native RAG and microservices
Mid-level AI/ML Engineer specializing in NLP, computer vision, and MLOps
Mid-level Full-Stack Java Engineer specializing in scalable microservices and real-time data systems
Mid-level Machine Learning Engineer specializing in LLMs and RAG systems
Intern AI/ML Engineer specializing in LLM agents, RAG, and computer vision
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG systems
Intern Software Engineer specializing in AI/ML and LLM retrieval systems
Senior Full-Stack Engineer specializing in AI/ML, LLMs, and RAG systems
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG