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Vetted Retrieval-Augmented Generation (RAG) Professionals
Pre-screened and vetted.
Senior Research Scientist specializing in LLM verification and fraud/risk modeling
Principal AI Systems Engineer specializing in enterprise identity security
Executive Technology Leader specializing in SaaS platforms and Generative AI
Mid-Level Software Engineer specializing in Cloud SRE and LLM-powered automation
Senior AI/ML Engineer specializing in foundation models, LLMs, and agentic AI
Director-level engineering leader specializing in cloud platforms, AI/ML, and SaaS
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level Python Backend Developer specializing in cloud-native microservices and AI/ML platforms
Senior Full-Stack Software Engineer specializing in FinTech payments and risk systems
Senior Software Engineer specializing in Python, cloud infrastructure, and AI-powered search
Principal/Staff Engineer specializing in platform architecture, AI/ML, and distributed systems
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”
Daming Jiang
Intern Software/AI Engineer specializing in LLM fine-tuning and agentic RAG systems
“Built and shipped an end-to-end LLM agent during an AT&T internship to automate network troubleshooting, with production-style reliability safeguards (timeouts/retries/fallbacks) and structured, state-machine orchestration; project won 3rd place in AT&T’s nationwide intern innovation challenge and was demoed to leadership. Also handled messy multi-partner data at Tencent by implementing schema validation/normalization, confidence-threshold fallbacks, and idempotent Python/ORM-based pipelines.”
Kalyani Chittipolu
Mid-level Data Engineer specializing in AI/ML platforms and cloud data pipelines
“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”
Jingfei Xu
Intern/Junior Software Engineer specializing in AI/ML and cloud-based systems
“Embedded/robotics software engineer with Hyundai Motors experience who owned an AI-driven perception validation pipeline using a Transformer-based approach to generate stable synthetic in-cabin audio for autonomy/ASR testing, cutting downstream testing time by 50%+. Has hands-on ROS integration (IMU sensor streaming, inference, control nodes), MQTT-based distributed messaging, and cloud/container deployment experience (Docker, Node/Express, AWS, CI/CD).”
Fernando Cardenas
Executive technology leader specializing in AI products and enterprise platform modernization
“Four-time founder with hands-on experience inside angel- and venture-backed executive teams, plus accelerator mentorship experience. Brings a practical, customer-validation-driven approach to company building, with strong insight into early-stage team dynamics and investor ecosystems.”