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Vetted Amazon SageMaker Professionals
Pre-screened and vetted.
Senior Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Junior AI/ML Engineer specializing in Generative AI production systems
Mid-level AI Engineer specializing in LLM safety, red teaming, and evaluation
Mid-Level Software Developer specializing in AI/ML and cloud-native microservices
Intern AI/ML Engineer specializing in NLP, graph analytics, and agentic RAG systems
Junior AI/ML Engineer specializing in LLM applications, RAG, and multimodal computer vision
Mid-Level Software Engineer specializing in ML and Generative AI applications
Mid-Level ML/AI Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in risk modeling, healthcare analytics, and MLOps
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Mid-Level Software Engineer specializing in AI/ML, cloud deployment, and full-stack systems
Mid-level Generative AI Engineer specializing in LLMs, RAG, and NLP systems
Senior Machine Learning Engineer specializing in Generative AI and MLOps
Tirth Shah
Mid-level AI/ML Engineer specializing in anomaly detection, data tooling, and cloud-native systems
“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”
Bhargav Kommineni
Intern Full-Stack/ML Engineer specializing in cloud-native web apps and LLM systems
“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”
Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”
SaiDheerajReddy Gadikota
Mid-level AI Engineer specializing in agentic systems and enterprise LLM platforms
“Current AI engineer at a startup who has spent the last year architecting multi-agent systems for software development workflows. Stands out for combining LLM speed with engineering discipline—using tools like Pydantic, LangGraph, and LangChain to build reliable, production-ready agent workflows with validation, routing, and retry logic.”
Sukhad Joshi
Junior Data Scientist specializing in applied ML, LLMs, and analytics automation
“Research Analyst at Syracuse who deployed an LLM-powered lab automation system allowing researchers to run QCoDeS instrument workflows via natural language, with strong safety guardrails for real instruments and multi-instrument support. Also collaborated with non-technical stakeholders at iConsult on an audio classification/recommendation pipeline, translating business goals into metrics and Tableau dashboards with model comparisons and A/B test results.”
“LLM/RAG engineer at Connex AI who built and deployed a production healthcare agent to extract clinical insights from medical data/notes. Strong focus on real-world reliability—hallucination mitigation (citations, schema validation, confidence thresholds, rejection logic), custom LangChain orchestration (query rewriting, fallback paths), and production evaluation/observability—while collaborating closely with clinical SMEs to ensure clinical fit and time savings.”