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Vetted Model Deployment Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in fraud detection and customer lifetime value modeling
Mid-level Data Scientist / ML Engineer specializing in NLP, recommender systems, and insurance analytics
Mid-level Data Scientist specializing in LLMs, RAG, and personalization
Junior Algorithm Engineer specializing in computer vision and 3D perception
Mid-level Data Scientist specializing in ML, NLP, and fraud/anomaly detection
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level Data Scientist specializing in NLP, deep learning, and big data analytics
Senior Data Scientist specializing in Generative AI/NLP for legal and healthcare domains
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Senior Software Engineer specializing in Python, AI/ML, and AWS cloud-native systems
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Mid-level Full-Stack Python Developer specializing in cloud-native FinTech and GenAI
Benjamin Kozel
Mid-level Machine Learning & Software Engineer specializing in RAG systems and ML infrastructure
“Built and deployed an in-house RAG LLM system ("MONTY") using LLaMA 3B + FAISS to help teams quickly understand long internal/external specifications. Delivered usable production performance despite severe compute limits (single RTX 3080) by tuning retrieval/reranking and model choice, and is planning a LightRAG/knowledge-graph rewrite to improve accuracy and latency.”
Shuju Sun
Mid-Level Software Engineer specializing in real-time data pipelines and ML deployment
“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”
Sri Lekha Kandadai
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
“ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.”
Meghashree N
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and cloud ML
“AI/ML engineer who has shipped both a safety-critical mental health RAG chatbot (Mistral 7B + Pinecone) with automated faithfulness/toxicity monitoring and a deep Q-learning investment recommendation engine at Lincoln Financial Group. Strong in production MLOps and orchestration (AWS Lambda/CloudWatch/SageMaker, Docker, AKS) and in translating regulated-domain requirements (clinical reliability, fiduciary duty) into measurable model constraints and monitoring.”
Amy Russ
Director-level Applied Science & AI/ML leader specializing in LLMs, RAG, and MLOps
“Active in the venture ecosystem as a Rogue Women's Fund fellow and angel investor, with memberships in Gaingels and Angel Squad (HustleFund). Interested in founding a company to leverage extensive experience, and evaluates ideas through market need and economic viability of the target population.”