.png){kind=logo}
Vetted FAISS Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs and RAG systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG systems
Senior Customer Success & Technical Account Leader specializing in AI/ML infrastructure
Mid-level Full-Stack Python Engineer specializing in AI search and distributed systems
Intern Software Engineer specializing in AI/ML and LLM retrieval systems
Senior Full-Stack Engineer specializing in AI/ML, LLMs, and RAG systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and GPU-accelerated cloud systems
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG
Senior Data Scientist specializing in Generative AI and conversational AI
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Venu Dave
Junior Software Development Engineer specializing in backend data platforms and LLM applications
“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”
Gowri Kajipuram
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning
“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”
Jeremiah Medina
Senior AI/ML Engineer specializing in LLMs, RAG, and cloud-native MLOps
“Built and owned a real-time clinical AI assistant at Andor Health, taking it from prototype through deployment, monitoring, and iterative improvement. Brings strong healthcare-focused GenAI experience across RAG, hybrid retrieval, LoRA fine-tuning, and production Python services, with measurable gains in accuracy, speed, and reliability.”
Ritesh Somashekar
Mid-level AI & ML Engineer specializing in NLP, LLMs, and scalable ML systems
“AI/ML engineer with experience spanning Accenture healthcare NLP systems, academic research, and Apple on-device LLM integration. Stands out for owning regulated production pipelines end-to-end—from HIPAA-compliant clinical NLP and EHR integrations to incident prevention, experiment tracking, and optimized on-device inference with LLaMA 3.”
Tanmayee Chandanam
Mid-level Data Scientist specializing in recommender systems, NLP, and real-time ML pipelines
“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”
Dhruv Arora
Senior Generative AI Implementation Consultant specializing in RAG and agentic AI on cloud
“LLM/RAG practitioner who built an AWS-based enterprise document search and summarization platform with RBAC and scaled it to 10K+ users, solving relevance issues via contextual chunking and hybrid retrieval. Also designed agentic workflows for a telecom forecast-validation use case using sub-agents, tool APIs, and strict context management, and has proven pre-sales influence (supported a $300K manufacturing deal with a roadmap-driven pitch).”