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Vetted Azure Machine Learning Professionals
Pre-screened and vetted.
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Mid-level Data Engineer specializing in cloud data platforms and FinTech analytics
Mid-level GenAI/ML Engineer specializing in LLM agents, RAG, and document intelligence
Senior Data Engineer specializing in Azure, Databricks, and BI/ETL platforms
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
Rithindatta Gundu
Mid-level AI/ML Engineer specializing in LLM systems and cloud MLOps
“Built a production LLM-powered fraud detection platform at Wells Fargo, combining OpenAI/Hugging Face models with RAG-based explanations to make flagged transactions interpretable for risk and compliance teams. Delivered low-latency, real-time inference at high scale on AWS (SageMaker + EKS), with strong observability and security controls, reducing manual reviews and false positives in a regulated environment.”
Naga Gayatri Bandaru
Mid-level AI/ML Engineer specializing in MLOps and production ML systems
“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”
Sharath Pampalker
Mid-level AI/ML Engineer specializing in real-time anomaly detection and AI agents
“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”
Ananya Bojja
Mid-level AI/ML Engineer specializing in healthcare analytics and MLOps
“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”