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Vetted Model Monitoring Professionals
Pre-screened and vetted.
Sourabh Jain
Director of Software Engineering specializing in enterprise Data, ML & AI platforms
“Former Walmart Director of Software Engineering who left in March 2025 to build products for clients. Recently delivered an LLM/RAG-based UNSPSC classification solution for an MRO client using a multi-stage retrieval + web search + prompt-engineering workflow, and has led large-scale retail forecasting initiatives and high-severity cloud-migration incidents end-to-end.”
BHARGAV KODURU
Mid-level Full-Stack Software Engineer specializing in cloud microservices and AI integration
“Backend/distributed-systems engineer with Uber experience building real-time telemetry and safety signal pipelines. Strong in Kafka-based event-driven architectures, low-latency processing under peak load, and production reliability via monitoring, retries, and fallback logic; has Docker/Kubernetes and CI/CD deployment experience.”
Asrith Velireddy
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems
“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”
Nikhel Parkar
Executive engineering leader specializing in FinTech, data platforms, and cloud modernization
“Aspiring founder building an AI governance and compliance startup for the finance industry, focused on agents that monitor data lakes/lakehouses to detect security vulnerabilities, PII exposure, and governance issues in real time. Has already formed an S-corp, has not raised capital yet, and approaches idea validation through Minimum Lovable Product testing with potential clients.”
Ranjani Salla
Mid-level AI/ML Engineer specializing in LLMs, FinTech, and Healthcare IT
“Built production GenAI systems in both healthcare and financial services, including a Verily clinical platform and an Accenture financial Q&A product. Stands out for combining advanced RAG, fine-tuning, safety evaluation, and infrastructure engineering to deliver measurable gains in engagement, groundedness, hallucination reduction, and cost efficiency.”
Yashwanth J
Mid-level Software Engineer specializing in AI/ML and full-stack systems
“Engineer with Apple experience building LLM-powered internal workflow orchestration systems using Python, LangGraph, FastAPI, Redis, vector search, and Kubernetes. Stands out for a highly pragmatic, production-focused approach to agentic systems: deterministic state management, strong guardrails, observability, and human review for high-risk actions.”
Chappidi Sasi
Mid-level Machine Learning Engineer specializing in GPU-accelerated LLM training and inference
“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”
Akhilesh Patil
Junior AI/ML Engineer specializing in FinTech and generative AI
“Built an end-to-end AI bug triage dashboard that combined React/TypeScript, FastAPI, Postgres, and classical ML to reduce manual engineering triage work by about 40%. Stands out for pragmatic, product-minded AI engineering: choosing interpretable models when they were sufficient, designing human-in-the-loop UX for trust, and separately building an agentic RAG project with vector search, Neo4j knowledge graphs, and reranking.”
Tanuja Ikkurthi
Mid AI/ML Engineer specializing in LLM systems and Generative AI
“Built and owned an LLM support copilot at Stripe focused on improving agent ticket resolution. Designed the backend and ML system end to end, using RAG, Redis caching, hybrid vector search, and LoRA fine-tuning to achieve 40% lower latency and 22% higher response accuracy, with continuous quality monitoring via Ragas and related evaluation frameworks.”
Mid-level Machine Learning Engineer specializing in deep learning, MLOps, and real-time inference
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and recommendation systems
Intern Machine Learning Researcher specializing in LLM and GNN security
Mid-level AI/ML Engineer specializing in fraud detection and customer lifetime value modeling
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and GPU-accelerated deep learning
Mid-level Data Scientist specializing in LLMs, RAG, and personalization
Mid-level Machine Learning Engineer specializing in LLM inference and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in MLOps, real-time ML, and LLM/RAG systems
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level Machine Learning Engineer specializing in recommender systems and LLM/RAG pipelines