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Vetted PySpark Professionals
Pre-screened and vetted.
Mid-Level Software Engineer specializing in FinTech trading and portfolio platforms
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Mid-level AI Engineer specializing in LLM agents, RAG, and enterprise GenAI
Mid-level AI/ML Data Engineer specializing in analytics, ML pipelines, and LLM applications
Junior Business Intelligence Engineer specializing in experimentation and causal inference
Mid-Level Software Engineer specializing in cloud-native distributed systems
Mid-level AI/ML Engineer specializing in MLOps, distributed ML, and RAG pipelines
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
Senior Machine Learning Engineer specializing in NLP, Generative AI, and healthcare/legal AI
VP Data Engineer specializing in AI-driven analytics platforms for investment management
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Mid-level Data Engineer specializing in cloud lakehouse and streaming pipelines
Mid-level Data Engineer specializing in streaming and cloud lakehouse platforms
Mid-level Data Engineer specializing in AWS, Spark, and streaming data pipelines
Mid-level Data Engineer specializing in cloud-native ETL and data warehousing
Senior AI Platform Engineer specializing in agentic AI and RAG systems
Mid-level Software Developer specializing in backend cloud and API platforms
Senior AI/ML Engineer specializing in Generative AI and cloud-native ML systems
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”
JYOTHI N
Senior Data Scientist specializing in analytics, experimentation, and BI on AWS
“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”