.png){kind=logo}
Vetted LoRA Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLMs, RAG, and distributed MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Intern Machine Learning Engineer specializing in LLMs, RAG, and model quantization
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps
Senior AI/ML Engineer specializing in GenAI, agentic systems, and healthcare AI
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).”
Sai supriya
Mid-level AI/ML Engineer specializing in LLM alignment, safety, and scalable inference
“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”
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.”
Mid-level Machine Learning Engineer specializing in LLMs and RAG systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Principal Full-Stack Software Engineer specializing in IoT/IIoT platforms
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and GPU-accelerated cloud systems
Mid-level Data Engineer specializing in cloud-native big data pipelines and analytics
Senior Machine Learning Engineer specializing in LLMs and Generative AI
Mid-level Computer Vision Engineer specializing in robotics perception and mapping