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Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
Mid-level AI/ML Engineer specializing in NLP, MLOps, and compliance-focused ML systems
Mid-level Data Scientist specializing in ML, MLOps, and forecasting for FinTech and AI hardware
Junior Machine Learning Engineer specializing in generative modeling and computer vision
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
Junior Data Scientist specializing in causal inference, NLP/LLMs, and forecasting
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and RAG for financial services
Junior Software Development Engineer specializing in ML, NLP, and data visualization
Mid-level AI/ML Engineer specializing in NLP, speech AI, and RAG systems
Mid-level Mechanical Engineer specializing in medical robotics and machine learning
Mid-level Full-Stack Engineer specializing in Python, FastAPI, and cloud-native systems
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Mid-level AI Engineer specializing in Generative AI and LLM/RAG systems
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Mid-level AI/ML Engineer specializing in forecasting, MLOps, and generative AI
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
Parvathi Primilla
Entry-level Robotics Engineer specializing in autonomous navigation and computer vision
“Robotics/IoT engineer who deployed a fog-enabled real-time monitoring system (edge Raspberry Pi + MQTT + cloud logging) and validated it via an IEEE-indexed publication. Strong in autonomous navigation with ROS/Gazebo, SLAM/localization, and cross-layer debugging using timing/transform-delay correlation. Extends Python computer vision pipelines (YOLO + OpenCV/Albumentations) for custom datasets and weather-specific conditions.”
Harbir Dhillon
Mid-level Software Engineer specializing in distributed systems and cloud-based full-stack development
“Software engineering candidate who built a compiler-like Python tool to translate between Python code and UML-style diagrams (and back). Also has hands-on AWS experience building a distributed pub/sub system using services like Lambda, API Gateway, ELB, WAF, VPC, and DynamoDB, plus ML projects using Kaggle datasets (e.g., diabetes risk analysis).”
Kartikeya Anand
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and multimodal modeling
“Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.”
Tejaswi Kothapalli
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and Conversational AI
“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”