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Vetted Semantic Search Professionals
Pre-screened and vetted.
Junior Full-Stack Engineer specializing in Python backends and AI search systems
Mid-level Applied AI Engineer specializing in LLMs, Prompt Engineering, and RAG
Mid-level AI & Data Engineer specializing in RAG and analytics platforms
Junior Software Engineer specializing in distributed systems, cloud, and LLM-powered search
Junior AI/ML Engineer specializing in RAG and multi-agent LLM systems
Mid-Level Software Engineer specializing in IoT platforms and data pipelines
Mid-level Machine Learning Engineer specializing in distributed AI systems
Mid-level Full-Stack Software Engineer specializing in AI-powered document platforms
Mid-level Software Engineer specializing in full-stack AI and healthcare platforms
Mid-level GenAI/ML Engineer specializing in enterprise LLM and RAG systems
Mid-level AI/ML Engineer specializing in fraud detection and enterprise ML systems
DhanushKautilya Kammaripalle
Junior AI Integration Engineer specializing in LLM agents and RAG on cloud platforms
“Built and deployed LLM-powered features for a startup organizational management application, focusing on real-world deployment constraints like latency and cost. Implemented RAG with FAISS and improved retrieval quality by switching embedding models (OpenAI/Hugging Face) and fine-tuning embeddings on medical corpora for a medical-report UI feature. Uses LangChain and LangGraph to orchestrate multi-node LLM API workflows and evaluates systems with metrics like latency, cost per request, and error taxonomy.”
Maneesh Ujji
Junior Machine Learning & Data Science professional specializing in AI agents and applied ML
“IT Analyst/research background with hands-on experience deploying and hardening a multi-agent AI support/triage system (ticket ingestion + knowledge-base retrieval) with strong emphasis on reliability and observability. Has debugged real production issues spanning backend services and network latency (sync failures/partial writes) and is comfortable in Linux environments; also has academic exposure to robotics simulation and ROS2.”
Snigdha Reddy Podduturi
Junior Data & AI Engineer specializing in cloud AI and analytics
“Built production AI backend systems in healthcare and e-commerce, including a healthcare agent that automated clinical workflows like medication refills, immunizations, and scheduling using FHIR APIs and cloud-native infrastructure. Strong in end-to-end backend ownership, LLM orchestration, and adding guardrails/validation for high-stakes and customer-facing AI workflows.”
Vikesh Patel
Senior AI/ML Engineer & Data Scientist specializing in LLMs, RAG, and MLOps
“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”
Saivedant Hava
Entry AI Engineer specializing in LLMs, RAG, and MLOps
“Built and shipped a production Python-based agentic RAG document retrieval system over 80K records using FastAPI, OCR, vector search, and AWS infrastructure, with a strong emphasis on reliability, testing, and observability. Stands out for treating AI failures like production incidents—turning hallucinations, retrieval misses, and OCR issues into regression tests—and for quantifiably reducing document lookup time from about 12 minutes to under 90 seconds.”
Sree Sai Preetham Nandamuri
Mid-level Data Scientist specializing in Generative AI and LLMOps
“Built a production-grade, semi-automated document recognition and classification system for large volumes of scanned PDFs, starting from little/no labeled data and handling highly variable scan quality. Deployed on AWS using SageMaker + Docker and orchestrated on EKS with a microservices design that scales CPU-heavy OCR separately from GPU inference, with strong reliability controls (validation, fallbacks, retries, readiness probes).”