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Vetted Anomaly Detection Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Junior Software Engineer specializing in Trust & Safety, QA automation, and ML
Mid-Level Software Engineer specializing in FinTech, Treasury, and Compliance Systems
Intern Machine Vision & Robotics Engineer specializing in computer vision and reinforcement learning
Mid-level Machine Learning Engineer specializing in recommender systems and LLM/RAG pipelines
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and RAG pipelines
Mid-level Machine Learning Engineer specializing in fraud prevention and LLM systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Performance & GPU Virtualization Engineer specializing in AI/ML and cloud certification
Senior Software Engineer specializing in Python, AI/ML, and AWS cloud-native systems
Junior Software Engineer specializing in cloud platforms, microservices, and AI/ML
Senior Data Analytics & Applied ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in multimodal and generative AI at scale
Mid-level Python Backend Developer specializing in FinTech and ML-driven fraud detection
Mid-level Full-Stack Engineer specializing in Python microservices and cloud automation
Pavan Devulapalle
Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development
“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Poorna Pedapudi
Mid-Level Software Engineer specializing in distributed backend systems and cloud-native microservices
“Software engineer focused on data platforms and applied LLM systems: built an internal data quality monitoring layer to catch silent data drift and iterated post-launch after finding ~30% false-positive alerts, reducing noise via dynamic baselines and improved structured logging. Also shipped a production RAG-based internal knowledge assistant over Jira/Confluence with citations, confidence-based fallbacks, and nightly automated evals to prevent regressions.”