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Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
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.”
Mid-level Data Scientist specializing in ML for healthcare and strategy analytics
Mid-level Machine Learning Engineer specializing in generative AI, NLP, and MLOps
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.”
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 AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native AI systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and on-device ML
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Junior Machine Learning Researcher specializing in biomedical AI and systems
Intern Software Engineer specializing in AI/ML and LLM retrieval systems
Mid-level Computer Vision Engineer specializing in robotics perception and mapping
Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”
Gowri Kajipuram
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning
“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”