.png){kind=logo}
Vetted Vector Search Professionals
Pre-screened and vetted.
Senior Applied AI Engineer specializing in LLMs, NLP, and production AI systems
Senior Software Engineer specializing in AI agents and cloud platforms
Mid-level Software Engineer specializing in backend, ML platforms, and FinTech
Senior Machine Learning Engineer specializing in LLM inference and GPU infrastructure
Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems
Senior AI/ML Engineer specializing in GenAI, agentic systems, and healthcare AI
Rana Taki
Junior Mechanical Engineering & Software Developer specializing in aviation autonomy and retrieval systems
“Robotics/embedded builder who trained an aviation-specific LLM and deployed it offline on an NVIDIA Jetson for an in-flight voice assistant, solving performance and cabling constraints with NVMe storage and Bluetooth. Also has hands-on Raspberry Pi/Arduino robot builds (including a cigarette-butt picking prototype with hydraulic actuation) plus Docker-based FEA work using FEniCS/Gmsh and strong CI/CD + automated testing practices.”
Nishitha Thummala
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference
“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”
Nikhil Reddy
Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms
“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Intern Full-Stack Engineer specializing in AI-driven RAG applications
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG systems
Senior Software Engineer specializing in AI/LLM and distributed cloud systems
Senior Software Engineer specializing in full-stack platforms, MLOps, and LLM search
Senior Data Scientist specializing in Generative AI and conversational AI
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.”
Alexander Choy
Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision
“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”