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Vetted Amazon Redshift Professionals
Pre-screened and vetted.
Staff Machine Learning Engineer specializing in Generative AI, MLOps, and Computer Vision
Mid-level Data Engineer specializing in cloud lakehouse and streaming analytics
Executive CTO/VP of Technology specializing in platform modernization, data, and healthcare SaaS
Senior Machine Learning Engineer specializing in computer vision and healthcare AI
Executive Technology & Data Leader specializing in AI/ML strategy and digital transformation
Senior Data Engineer specializing in cloud lakehouse platforms and healthcare data
Mid-level AI Data Engineer specializing in real-time streaming and LLM-powered fraud analytics
Junior Software Engineer specializing in data engineering and machine learning
Senior Machine Learning Engineer specializing in MLOps and GenAI platforms
Senior Backend Software Engineer specializing in FinTech and distributed systems
Mid-level Data Engineer specializing in real-time streaming and cloud data platforms
Mid-level Analytics Engineer specializing in dbt, SQL transformation, and Snowflake
Director of Architecture specializing in AdTech, AI/ML, and large-scale cloud platforms
Executive IT & Software Development Leader specializing in cloud-native transformation and trading platforms
Mid-level Financial Analyst specializing in banking and credit-risk analytics
Senior Site Reliability Engineer specializing in multi-cloud, Kubernetes, and observability
Senior Data Engineer specializing in cloud data platforms and big data pipelines
Devisri Veeramachaneni
Senior Software Engineer specializing in cloud backend systems and LLM-powered agents
“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”
Sahithi S
Mid-level AI/ML Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”
Leela Tikkisetty
Mid-level Software Engineer specializing in ML platforms and cloud-native backend systems
“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”