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Vetted Retrieval-Augmented Generation Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and LLM applications
Mid-level Data Scientist specializing in ML, NLP, and scalable data pipelines
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and Generative AI
Mid-level AI/Data Engineer specializing in LLMs, RAG pipelines, and cloud data platforms
Mid-Level Full-Stack Software Engineer specializing in distributed systems and FinTech
Senior AI Engineer specializing in LLM and generative AI production deployments
Junior Data & AI Analyst specializing in BI, LLM applications, and analytics
Mid-level Full-Stack Java Developer specializing in cloud microservices and React
Mid-level Data Engineer specializing in cloud-native ETL/ELT and Snowflake analytics platforms
Mid-level AI Software Engineer specializing in agentic AI, RAG, and data engineering
Mid-level Software Engineer specializing in AI and cloud data platforms
Mid-level AI Engineer specializing in machine learning and generative AI
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Senior Data Strategy & AI Product Consultant specializing in analytics platforms and privacy-safe measurement
Executive technology leader specializing in AI-driven HealthTech and SaaS
Tushar Gwal
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and MLOps
“AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.”
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”