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Vetted Scala Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG pipelines
Mid-level Data Engineer specializing in cloud-native data platforms and streaming pipelines
Junior Data Engineer specializing in cloud ETL/ELT and big data pipelines
Mid-level AI/ML Engineer specializing in Generative AI, healthcare, and FinTech
Mid-level Machine Learning Engineer specializing in LLM, RAG, and conversational AI systems
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Senior Data Engineer/Data Scientist specializing in ML platforms, cloud data, and blockchain analytics
Senior Lead Data Engineer specializing in cloud data platforms and real-time ML pipelines
Mid-level Machine Learning Engineer specializing in Generative AI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG for healthcare
Mid-Level Software Engineer specializing in distributed systems and AI/ML
Mid-level Data Science & AI/ML Engineer specializing in MLOps, NLP, and computer vision
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Junior AI Engineer specializing in LLM systems, RAG, and production data pipelines
Senior Data Analyst specializing in BI, data engineering, and predictive analytics
Naresh Vemula
Mid-level Data Engineer specializing in Cloud & Big Data ETL/ELT
“Data engineer in financial services (Northern Trust) who has worked across ingestion, transformation, data quality, orchestration, and serving on AWS (S3/Glue/EMR) with Airflow. Highlights include processing ~15M transactions with validation/anomaly detection for regulatory reporting and improving Snowflake query performance by 27% for risk/compliance reporting. Also built a personal real-time streaming service (FastAPI, Kafka, Redis, Cassandra) and uses production reliability patterns like blue-green/atomic swaps and robust retry strategies.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Mid-Level Software Engineer specializing in distributed systems and event-driven microservices
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines