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Vetted FAISS Professionals
Pre-screened and vetted.
Junior Full-Stack Software Engineer specializing in web, mobile, and AI applications
Junior Software Engineer specializing in AI/ML and FinTech systems
Mid-level AI/ML Engineer specializing in GenAI, RAG, and cloud-native ML platforms
Intern Machine Learning Engineer specializing in recommender systems and financial risk modeling
Mid-level Software Engineer specializing in backend systems, cloud microservices, and AI-driven automation
Mid-Level Software Engineer specializing in cloud backend and GenAI assistants
Mid-level AI/ML Engineer specializing in NLP, RAG, and agentic AI
Senior Full-Stack & AI/ML Engineer specializing in cloud-native platforms and LLM systems
Mid-level AI Engineer specializing in LLM agents, RAG, and enterprise GenAI
Intern Software Engineer specializing in cloud governance and distributed systems
Senior AI Platform Engineer specializing in agentic AI and RAG systems
Executive CTO/VP Engineering specializing in high-performance AI, data systems, and distributed infrastructure
Rubeena Riyas
Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms
“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”
Abhishikth Meesala
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”
JYOTHI N
Senior Data Scientist specializing in analytics, experimentation, and BI on AWS
“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”
“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”