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Vetted Machine Learning Professionals
Pre-screened and vetted.
Engineering Leader & Principal Software Engineer specializing in cloud-native SaaS
Mid-Level Software Engineer specializing in distributed systems and event-driven microservices
Mid-level Backend Software Engineer specializing in distributed systems and cloud-native microservices
Principal Software Architect specializing in Healthcare IT and cloud-native systems
Senior Product & Program Management leader specializing in AI/ML and digital transformation
Intern Software Engineer specializing in AI/ML and data-driven web tools
Senior Product Manager / Project Manager specializing in data platforms, BI, and cloud transformation
Senior Software Engineer specializing in backend systems, cloud infrastructure, and applied AI/ML
Senior Backend Engineer specializing in cloud-native microservices and AI integrations
Executive Technology Leader (CTO/VP Engineering) specializing in AI-driven commerce platforms
Executive AI/ML & Platform Technology Leader specializing in LLMs, GraphRAG, and security
Mid-level Machine Learning Engineer specializing in healthcare and enterprise analytics
Mid-level Full-Stack Developer specializing in cloud-native web apps and FinTech
Senior Software Engineer / DevOps specializing in cloud-native distributed systems
Mid-level AI/ML Engineer specializing in financial risk, NLP, and MLOps
Junior Software Engineer specializing in backend systems, QA automation, and AI/ML
Executive Engineering Leader specializing in Product, Mobile, and SaaS platforms
Senior Full-Stack Java Developer specializing in AWS cloud and microservices
Executive Technology Leader specializing in HPC/AI, distributed systems, and trusted computing
VamshiKrishna Challa
Mid-level Data Scientist specializing in industrial IoT, predictive analytics, and generative AI
“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”
Alexis Abbott
Senior Python Developer specializing in AWS, microservices, and data pipelines
“Backend/data engineer with strong AWS production experience spanning serverless APIs and containerized workers (Lambda, API Gateway, ECS) plus data pipelines (Glue, S3, Athena/Redshift). Has modernized legacy SAS/cron batch systems into Python/AWS with parallel-run parity validation and low-risk cutovers, and has owned ETL incidents end-to-end (CloudWatch detection, backfills, and preventative controls). Targeting $130k–$150k base and strongly prefers remote, with occasional Bethesda onsite acceptable.”