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Vetted FAISS Professionals
Pre-screened and vetted.
Mid-level Full-Stack & AI Backend Engineer specializing in LLM/RAG systems
Staff Full-Stack Software Engineer specializing in AI-driven platforms
Intern AI Researcher specializing in NLP, multimodal generative AI, and medical imaging
Mid-Level Software Development Engineer specializing in AWS serverless, security, and ML platforms
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and RAG pipelines
Mid-level Machine Learning Engineer specializing in fraud prevention and LLM systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Senior Software Engineer specializing in Python, AI/ML, and AWS cloud-native systems
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Senior Data Analytics & Applied ML Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in NLP, transformers, and RAG systems
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Peeyush Dyavarashetty
Intern AI/ML Engineer specializing in GenAI, LLMs, and agentic RAG systems
“AI/LLM practitioner who built a GPT-2-like language model from scratch at the University of Maryland using PyTorch and multi-GPU distributed training, with experiment tracking in Weights & Biases. As an AI Operations intern at ScaleUp360, delivered multiple production-style AI agent automations (Gmail classification and Fireflies-to-Claude workflows that extract and assign CEO tasks) and set up measurable evaluation using test cases and classification metrics.”
Gagan Mundada
Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks
“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”
Vignesh Shanmugasundaram
Junior Software Engineer specializing in full-stack development and applied ML
“Full-stack engineer with experience at Zoho and Amazon who has owned production systems end-to-end, including a monolith-to-microservices migration using Kafka and Cassandra that improved search latency ~25% and increased throughput without data loss. Also built a hackathon project (Buildwise) into a sold product for a construction company (AI-driven document compliance checks) and shipped an IoT-based parking availability MVP in 3 weeks.”
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Benjamin Kozel
Mid-level Machine Learning & Software Engineer specializing in RAG systems and ML infrastructure
“Built and deployed an in-house RAG LLM system ("MONTY") using LLaMA 3B + FAISS to help teams quickly understand long internal/external specifications. Delivered usable production performance despite severe compute limits (single RTX 3080) by tuning retrieval/reranking and model choice, and is planning a LightRAG/knowledge-graph rewrite to improve accuracy and latency.”
Darsh Sharma
Mid-level Software Engineer specializing in ML systems and microservices
“Teradata Text Security intern who built a production LLM-powered planner agent that decomposes complex tasks into dependency-aware subtasks (DAG/topological graph) and executes them via a custom orchestrator with parallelism, status tracking, and error handling. Also contributed to an HR-facing internal document chatbot concept to streamline onboarding, showing cross-functional collaboration.”