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Vetted FAISS Professionals
Pre-screened and vetted.
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.”
Krishna Reddy
Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants
“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”
Alfred Dwan
Intern Software Engineer specializing in AI, cloud-native systems, and MLOps
“Backend/full-stack engineer who has owned a production recruiting platform end-to-end (TypeScript/Node microservices for scraping/cleaning/serving job data, RabbitMQ for spike handling, MongoDB + Elasticsearch, AWS containers) with pragmatic CI, logging/alerts, and Docker Compose E2E tests. Also operated high-traffic event pipelines during a Binance internship using Kafka + Redis idempotency, with strong observability and failure-mode/rollback/degradation practices, and has experience designing developer-friendly REST APIs and resilient browser automation for E2E flows.”
Krishna Sahith Poruri
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”
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 Software Development Engineer specializing in cloud platforms, data engineering, and LLM apps
Mid-level AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems
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
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