.png){kind=logo}
Vetted Apache Hive Professionals
Pre-screened and vetted.
Mid-level Data Analyst specializing in growth, product, and healthcare analytics
Junior Machine Learning Engineer specializing in NLP and computer vision
Mid-level Software Engineer specializing in real-time streaming and analytics platforms
Junior Data Analyst specializing in AI analytics and BI for financial services
Mid-Level Backend Engineer specializing in cloud-native distributed systems and data pipelines
Mid-level AI & Machine Learning Engineer specializing in production ML and LLM applications
Senior Software Engineer specializing in cloud-native distributed systems and AI/ML platforms
Senior Data Engineer specializing in cloud data platforms and scalable ETL pipelines
Senior Software Engineer specializing in backend systems and payment platforms
Mid-level AI/ML Engineer specializing in production ML, NLP, and computer vision
Mid-level Machine Learning Engineer specializing in GenAI, LLM agents, and MLOps
Staff Machine Learning Engineer specializing in Generative AI, MLOps, and Computer Vision
Senior Software Developer specializing in Python, AWS, and Big Data
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
Mid-level Data Engineer specializing in real-time streaming and cloud data platforms
Senior Data Engineer specializing in cloud data platforms and big data pipelines
Nandini Kosgi
Mid-level AI/ML Engineer specializing in LLMs, RAG, and fraud/risk analytics in Financial Services
“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”
Sai Krishna Yemineni
Mid-level AI/ML Engineer specializing in healthcare NLP, real-time risk systems, and ML platforms
“LLM-focused customer-facing engineer who repeatedly takes document Q&A and agentic prototypes into secure, monitored production systems. Experienced in reducing hallucinations via RAG + guardrails, diagnosing retrieval/embedding issues in real time, and partnering with sales to run metrics-driven PoCs that overcome accuracy/security objections and drive adoption.”
Sahithi K
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
“Data engineer with experience at Moderna and Block owning high-volume (≈10TB/day) production pipelines on AWS, using Kafka/S3/Glue/dbt/Snowflake with strong data quality and observability practices (schema validation, anomaly detection, CloudWatch monitoring). Also built external financial API ingestion with Airflow retries, throttling/token rotation, and schema versioning, and helped stand up an early-stage biomedical data platform with CI/CD and incident debugging.”
Lalithya Manasa Patri
Senior Data Engineer specializing in cloud ETL and real-time streaming pipelines
“Data engineer with eBay experience owning end-to-end pipelines for real-time order and user behavior analytics at 10M+ records/day. Strong in PySpark/SQL transformations, Airflow reliability patterns, and production observability (CloudWatch), with measurable outcomes including improved data quality and 30–40% query performance gains. Also built Python data APIs for analytics/ML consumers with versioning and backward compatibility.”
Travoy Spelling
Senior Data Scientist / ML Engineer specializing in GenAI, LLMs, and NLP
“ML/NLP engineer focused on production GenAI and data linking systems: built a large-scale RAG pipeline over millions of support docs using LangChain/Pinecone and added a LangGraph-based validation layer to cut hallucinations ~40%. Also built scalable PySpark entity resolution (95%+ accuracy) and fine-tuned Sentence-BERT embeddings with contrastive learning for ~30% relevance lift, with strong CI/CD and observability practices (OpenTelemetry, Prometheus/Grafana).”