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Vetted Amazon Redshift Professionals
Pre-screened and vetted.
Mid-level Data Engineer specializing in cloud data platforms for Healthcare and Financial Services
Mid-level Data Engineer specializing in cloud lakehouse and real-time streaming
Senior Data Engineer specializing in multi-cloud lakehouse architectures and privacy/AI governance
Junior Data Engineer specializing in cloud data platforms and MLOps
Mid-level Data Analyst specializing in healthcare and financial analytics
Mid-level Data Analyst specializing in business intelligence and predictive analytics
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Mid-level Full-Stack Engineer specializing in FinTech and cloud-native applications
Mid-level Software Engineer specializing in distributed backend systems for FinTech
Principal AI Architect specializing in enterprise GenAI transformation
Mid-level Data Engineer specializing in AWS data lakes for healthcare and financial services
Director-level Engineering Leader specializing in cloud platforms, AI/ML, and scalable SaaS
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
Senior Full-Stack Java Developer specializing in Spring Boot microservices and cloud platforms
GOWRI SHANKAR ANANTHULA
Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG
“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”
Ankita Mungalpara
Mid-level Data Scientist specializing in Generative AI and multimodal systems
“Recent J&J intern who built a conversational RAG agent and led a shift from a monolithic model to a modular RAG workflow, cutting response time from several days to under a second by tackling data fragmentation, context retention, and embedding/latency optimization. Also worked on a large (7B-parameter) multimodal VQA pipeline for healthcare research and stays current via NeurIPS/ICLR and open-source contributions.”
Vardhan Are
Mid-level Data Analyst specializing in AWS-based ETL, churn analytics, and BI dashboards
“Data/ML practitioner with experience at Airtel and Lincoln Financial delivering measurable business outcomes: improved retention 15% via NLP sentiment analysis and cut response time ~25% using sentence-BERT + FAISS semantic linking. Strong in data quality/identity resolution (SQL + fuzzy matching) and in building production-grade Python workflows orchestrated with Airflow/AWS Glue, including validation and dashboard integration in Power BI.”
Sri Niyati Kompella
Senior Data Engineer specializing in cloud data platforms and ML pipelines
“Data engineer focused on AWS-based enterprise data platforms, owning end-to-end pipelines from multi-source batch/stream ingestion (Glue/Kinesis/StreamSets/Airflow) through PySpark transformations into curated datasets for Redshift/Snowflake. Emphasizes production reliability with strong monitoring/observability and data quality gates, and reports ~30% performance improvement plus improved SLAs and latency after optimization.”
Rama Gowtham Reddy Padala
Mid-level Backend Python Engineer specializing in APIs, microservices, and data pipelines
“Backend engineer (Marsh McLennan) who evolved a high-volume claims automation pipeline in Python, emphasizing thin APIs with background job processing, strong validation/retries, and production-grade observability. Experienced in secure FastAPI API design (centralized JWT/RBAC), multi-tenant Postgres/Supabase-style row-level security, and low-risk refactors using parallel runs and feature flags; targeting founding-engineer scope roles.”