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Vetted Model Monitoring Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Executive Technology Leader specializing in regulated multi-tenant SaaS and LLM-enabled products
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Mid-level Data Scientist specializing in ML, MLOps, and applied risk modeling
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Applied AI Engineer specializing in Generative AI and RAG systems
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.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”
Siva Pothuru
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML
“LLM/agent engineer at USAA who built a production GPT-4o RAG conversational assistant for financial analysts, focused on regulatory interpretation and internal documentation search. Emphasizes compliance-grade reliability with strict grounding, safe fallbacks, and full auditability via MLflow/DVC plus human-in-the-loop review; reports ~45% reduction in ticket resolution time.”
Mid-level AI/ML Engineer specializing in generative AI and MLOps
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level Data Scientist / Machine Learning Engineer specializing in NLP and computer vision
Mid-level Machine Learning Engineer specializing in MLOps and applied data science
Mid-level Full-Stack Software Engineer specializing in GenAI and SaaS platforms
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Nimsy Duddu
Mid-level AI/ML Engineer specializing in LLMs, RAG, and cloud MLOps
“Backend engineer with insurance/claims domain experience who modernized legacy claims processing systems to support AI-assisted claim review. Emphasizes production-ready API design in Python/FastAPI (schemas, async, caching, graceful degradation), strong observability with Prometheus, and layered security including JWT auth plus database row-level security (Supabase/Postgres).”
Sai Santosh Vasamsetti
Mid-level Software Engineer specializing in full-stack and machine learning
“Built a production AI-powered customer support Q&A system using an internal knowledge base to reduce repetitive ticket work and improve customer satisfaction, with an emphasis on source-backed answers and expert oversight. Also has experience defining deployment services in a microservices architecture and integrating large-scale APIs (including work connected to US HHS/COVID-19).”