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Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Junior Software Engineer specializing in distributed systems and AI evaluation
Senior AI/ML Engineer specializing in Generative AI, NLP, and LLM systems
Junior Software Engineer specializing in ML, computer vision, and data engineering
Mid-level Data Scientist specializing in LLMs, RAG, and personalization
Mid-level Data Scientist specializing in ML, NLP, and fraud/anomaly detection
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and RAG pipelines
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Mid-level Machine Learning Engineer specializing in MLOps and scalable ML pipelines
Mid-level AI/ML Engineer specializing in NLP, transformers, and RAG systems
Mid AI/ML Engineer specializing in LLM systems and inference optimization
Surya Singh
Mid-level AI/ML Engineer specializing in FinTech and fraud detection
“ML/backend engineer with PayPal experience building high-stakes production systems, including a GenAI internal support assistant and a real-time fraud scoring pipeline. Strong in Python/FastAPI, model-serving infrastructure, RAG architecture, and production observability, with clear readiness to transition those backend patterns into a TypeScript stack.”
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Xinyuan Lin
Intern Software Engineer specializing in LLMs, RAG, and full-stack systems
“Built and productionized a multi-agent LLM analytics assistant at eBay that routes natural-language questions to retrieval or text-to-SQL, dynamically retrieves relevant schemas via a vector DB, and executes against a data warehouse. Drove a major quality lift (text-to-SQL accuracy 60%→85%) and materially reduced time engineers/PMs spent getting data insights through strong eval/monitoring, tracing, and reliability-focused design (schema retrieval, strict JSON outputs, retries/clarifications).”
Zahan Goel
Junior AI/ML Engineer specializing in LLM systems and mechanistic interpretability
“Second most active contributor at Daice Labs, owning a production AI-powered software development collaboration platform’s end-to-end execution infrastructure (TypeScript/Next.js backend, Node.js CLI, shared libs). Built the full multi-agent pipeline (planning/codegen/summary), Supabase-backed context assembly and realtime state, Git/GitHub automation, and a provider-agnostic LLM abstraction with strict Zod validation and retries, backed by extensive tests and design specs.”
Vikranth Gurram
Machine learning engineer and software developer with experience across fintech, e-commerce, and gaming.
“ML/AI engineer with hands-on ownership of production systems spanning classical ML fraud detection and GenAI agent workflows. At Fidelity, they built an end-to-end fraud platform that improved review queue Precision@K by 15-20% while reducing false positives 10-15%, and they also shipped RAG-based agent systems that cut manual workflow effort by 30-40%.”
Meghashree N
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and cloud ML
“AI/ML engineer who has shipped both a safety-critical mental health RAG chatbot (Mistral 7B + Pinecone) with automated faithfulness/toxicity monitoring and a deep Q-learning investment recommendation engine at Lincoln Financial Group. Strong in production MLOps and orchestration (AWS Lambda/CloudWatch/SageMaker, Docker, AKS) and in translating regulated-domain requirements (clinical reliability, fiduciary duty) into measurable model constraints and monitoring.”
Rojin Bakhti
Junior Software Engineer specializing in Edge AI and ML deployment
“Qualcomm engineer building Android applications that run on Qualcomm AI accelerators, with hands-on experience in C++ concurrency, chipset stress testing, and power/performance tuning. Has deployed on-device AI models and built deployment/log post-processing workflows using Docker/Kubernetes and CI/CD; interested in translating this embedded AI/performance background into robotics (perception/real-time systems).”