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Vetted Model Monitoring Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Priyanka Kaswan
Senior AI Research Engineer specializing in LLM agents and large-scale ML
“AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.”
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”
Sason Torosean
Principal Data Scientist specializing in financial risk, forecasting, and applied ML
“ML/NLP practitioner and technical founder who built an AUP risk-scoring model at Bill.com using TF-IDF + SVD features with XGBoost, and previously created automated data-quality guardrails for a Global Equity Risk stacked ML model at Thomson Reuters. Recently built a RAG-based chatbot for PaymentJock’s Home Affordability Probability product using embeddings and a local vector database (FAISS/Chroma), improving answer quality through chunking rather than expensive fine-tuning.”
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.”
Shashank Sathola
Mid-level Python Backend Developer specializing in cloud-native microservices and AI/ML platforms
“Backend/AI engineer who built a production GPU-backed real-time inference API at Nvidia and debugged burst-induced tail latency, cutting P95 by ~29% through dynamic batching and backpressure. Also shipped an end-to-end RAG + agentic operational diagnostics assistant with strict tool controls, evidence citation, confidence gating, and strong production guardrails, plus demonstrated hands-on Postgres optimization (900ms to 40–60ms).”
Mid-level Machine Learning Engineer specializing in MLOps and cloud-native ML systems
Senior Data Scientist specializing in NLP, LLMs, and Generative AI automation
Senior AI/ML Software Engineer specializing in LLMs, NLP, and scalable ML platforms
Senior Applied ML Scientist specializing in LLMs, ads ranking, and RAG systems
Mid-level Machine Learning Engineer specializing in LLMs, ranking, and scalable ML systems
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal recommendation systems
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Senior Data Scientist specializing in large-scale ML systems and recommendations
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
Parth Vadera
Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps
“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”