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Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Mid-level AI/ML Engineer specializing in cloud MLOps, LLM agents, and risk & fraud modeling
Mid-level AI/ML Engineer specializing in generative AI and cloud ML platforms
Mid-level AI Engineer specializing in LLMs, RAG, and multi-agent systems
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
Rodolfo Lopez
Senior Math Educator transitioning to Data Science & Business Analytics
“Recent McCombs School of Business (UT Austin) Post Graduate Program graduate in Data Science & Business Analytics with hands-on project experience spanning stock clustering/segmentation and hotel booking-cancellation prediction. Strong in end-to-end analysis workflows (EDA, cleaning, feature engineering) and rigorous model comparison/selection, with exposure to boosting methods and imbalanced-data techniques; limited experience so far with embeddings/vector databases and production deployment.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
William Armstrong
Junior Product Engineer specializing in AI and SaaS
“Product intern at an AI startup (AdvisorGPT) who helped turn an LLM-based prototype into a production SEO blog-generation workflow that matched a firm’s tone/voice and targeted specific search phrases. Strong at bridging technical and non-technical teams, rapidly learning new AI tooling, and driving adoption through customer calls, UX improvements, and customer-facing demos/workshops.”
Khuram Ismaeel
Senior AI/ML Engineer specializing in machine learning and cloud-native AI systems
“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”
Mid-level AI/ML Engineer specializing in generative AI and MLOps
Mid-level Data Scientist / Machine Learning Engineer specializing in NLP and computer vision
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Nagaraju Kanubuddi
Mid-level AI/ML Engineer specializing in fraud detection, recommender systems, and forecasting
“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”
shubham patil
Mid-level AI Engineer specializing in Generative AI, RAG systems, and fraud analytics
“Built and deployed a RAG-based student/faculty support chatbot at a university that answers from official syllabus/policy documents and now supports 4,000+ students while reducing repetitive support requests. Hands-on with LangChain, LangGraph, and CrewAI to orchestrate reliable agentic workflows, with a strong focus on testing/monitoring in production and cross-functional delivery (e.g., marketing analytics automation at Steve Madden).”
Mounika Yalamanchili
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”
Uttam Kumar
Intern AI Engineer specializing in LLM agents, RAG, and scalable cloud deployment
“AI/LLM engineer at GPT integrators who built a production multi-agent enterprise workflow integration system, tackling hard problems in agent orchestration, layered memory, and custom RAG over enterprise/user data. Also built an education-focused agent solution integrating with Canvas, Zoom, and email to automate classroom admin tasks, and is currently applying agentic AI to insurance underwriting workflows in collaboration with underwriters.”
Deepak Singh
Mid Software Engineer specializing in systems, CI/CD, and applied machine learning
“Engineer at Syniti who uses AI tools pragmatically to speed development while maintaining quality through rigorous validation, code reviews, and CI/CD. Most notably, they leveraged AI-assisted testing to increase test coverage from 10% to 70%, and they are actively exploring more advanced agent-based development workflows.”