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Vetted FAISS Professionals
Pre-screened and vetted.
Junior Full-Stack Software Developer specializing in cloud-native apps and data/AI
Junior Machine Learning Engineer specializing in LLMs, RAG, and fine-tuning
Intern AI/ML Software Engineer specializing in NLP and model serving
Mid-level AI/ML Engineer specializing in LLMs and RAG systems
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems
Mid-level Machine Learning Engineer specializing in Generative AI and healthcare NLP
Entry-Level Software Engineer specializing in backend services and applied ML
Manas Kumar Sherla
Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI
“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”
Entry-level Generative AI Developer specializing in LLM agents and RAG systems
Junior Software/AI Engineer specializing in GPU-accelerated HPC and machine learning
Junior Machine Learning Engineer specializing in Agentic RAG and Document AI
“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”
richi ibnu wardana
“Built an automated ML/NLP document classification system for unstructured legal documents, combining classical models (TF-IDF + logistic regression/random forest) with entity resolution via fuzzy matching validated by precision/recall. Also implemented semantic similarity search using sentence embeddings stored in FAISS and improved matching by fine-tuning a transformer on domain-specific data and tuning similarity thresholds for fewer false positives.”
Intern Machine Learning Engineer specializing in NLP, RAG, and time-series forecasting