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Vetted LoRA Professionals
Pre-screened and vetted.
Yue Zhao
Junior Machine Learning Researcher specializing in multimodal LLMs and computer vision
“LLM/multimodal systems builder who developed DuetGen, a practical multimodal interleaved text-image generation system using a decoupled MLLM planner and video-pretrained diffusion transformer for high-quality image generation with step-wise alignment. Built a 298K-sample interleaved dataset across 8 domains/151 subtasks and deployed a GPT-5-based automated evaluation framework; also has LangChain-based multimodal agent orchestration experience with custom state management and reliability testing.”
Abhinav Bandaru
Junior Data Scientist specializing in Generative AI and agentic LLM systems
“LLM/agentic-systems builder who has shipped production tools for investment research and procurement insights, including a company screener that processes thousands of conference-listed companies using FireCrawl + Google Search + Gemini. Demonstrates strong orchestration expertise (LangGraph multi-agent graphs), performance optimization (async/batching to sub-30s), and pragmatic reliability/evaluation practices with stakeholder-friendly UX (real-time cost tracking and model/parameter toggles).”
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.”
Anand Vallamsetla
Executive Engineering Leader & Systems Architect specializing in AI, cloud platforms, and FinTech
“Operator with 20+ years experience and a business degree who led the build and deployment of a Medicaid fraud investigation product for Texas HHSC OIG, cutting investigation time from ~2 years to 90 days. Has government go-to-market experience and has raised a friends-and-family round for an earlier startup concept; now pivoting toward a healthcare-focused venture after a cofounder with core tech could not continue.”
Subhash Mothukuru
Mid-level Machine Learning Engineer specializing in LLMs, generative AI, and MLOps
“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”
Sahil Bansal
Mid-level Backend & ML Engineer specializing in LLM systems and scalable AI pipelines
“Built and shipped a real-time AI phone agent for small businesses that handles bookings/FAQs/messages using streaming ASR, an LLM with tool-calling, and TTS; deployed to production for multiple paying customers. Demonstrates strong applied LLM reliability practices (tool-first grounding, retrieval, hard-negative testing, and production monitoring) and experience orchestrating multi-step AI workflows with Airflow, Prefect, and AWS Step Functions.”
Pankaj Goyal
Director-level Engineering Leader specializing in FinTech, IAM, and AI/ML platforms
“Player-coach backend leader at PostLo who led a major backend architecture upgrade to enable AI-driven features by separating transactional systems from AI workloads (vector embeddings/image validation) and adding async processing for heavy jobs. Also owned production reliability improvements (query/index optimization, workload isolation, monitoring and load testing) and translated an ambiguous retention goal into a shipped cashback rewards feature with auditable transactions.”
Mid-level AI/ML Engineer specializing in GPU-accelerated LLMs, RAG, and production MLOps
Intern Machine Learning & Cloud Engineer specializing in cloud-native deployment and forecasting
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and multi-agent systems
Mid-level AI/ML Engineer specializing in LLMs, search ranking, and multimodal ML
Senior Agentic AI & Backend Engineer specializing in LLM platforms and multi-agent systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Senior Machine Learning Scientist specializing in LLMs, RAG, and health AI
Dennis Noto
Executive Technology & Security Leader specializing in FinTech, AI platforms, and enterprise modernization
“Technology transformation leader who builds board-approved roadmaps and scales engineering orgs with strong Agile execution. Led large modernization efforts (e.g., Scottrade: 3,000 programs/4M LOC in 18 months) and scaled POCs into enterprise SaaS platforms using Docker, Kubernetes, Helm, and Terraform for high-concurrency workloads.”
Harish Kasu
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”
Yishi Wang
Junior Machine Learning & Data Science professional specializing in LLMs and analytics
“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”