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Vetted Amazon Redshift Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in cloud-native microservices and real-time data pipelines
Mid-Level Full-Stack Software Engineer specializing in microservices and fraud detection
Mid-Level Full-Stack Software Engineer specializing in microservices and cloud
Mid-level DevOps & MLOps Engineer specializing in cloud-native CI/CD and Kubernetes
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level Data Scientist specializing in ML, MLOps, and forecasting for FinTech and AI hardware
Senior Full-Stack Engineer specializing in cloud architecture and AI/ML integration
Mid-level Full-Stack Java Developer specializing in microservices and cloud platforms
Mid-level Full-Stack Software Engineer specializing in cloud-native data platforms
Mid-Level Full-Stack Software Engineer specializing in Java/Spring Boot microservices
Mid-level Data Engineer specializing in cloud-native ETL and data warehousing
Senior Full-Stack Software Engineer specializing in cloud microservices and GenAI
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
NEHA KOLAN
Mid-Level Software Engineer specializing in microservices and cloud data pipelines
“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”
Swathi Sankaran
Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI
“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”
Anvith Reddy Dodda
Mid-level AI Engineer specializing in GenAI, NLP, and MLOps
“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”
Timothy Wong
Mid-level Data Engineer specializing in experimentation, analytics, and AI-driven product experiences
“Built production LLM automations using the Claude API, including a sales enablement workflow that summarizes playbooks and incorporates sales call metadata into strategic one-pagers. Experienced in orchestrating and scheduling data pipelines with SnapLogic, Airflow, and Databricks, and in scaling LLM API calls via parallel/batch processing. Also partnered with HR to deliver prompt-tuned, automated Slack messaging aligned to business tone and acceptance criteria.”
“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”
Sasi Katamneni
Mid-level Data Scientist / AI-ML Engineer specializing in Generative AI and LLM applications
“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”