.png){kind=logo}
Vetted Transformers Professionals
Pre-screened and vetted.
Mid-level Data Scientist / AI/ML Engineer specializing in financial services and GenAI
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Data Scientist specializing in ML, MLOps, and applied risk modeling
Mid-level Data Scientist specializing in GenAI, NLP, and cloud MLOps
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Entry-level Machine Learning Engineer specializing in LLM systems and AI infrastructure
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Anju Vilashni Nandhakumar
Entry-level Machine Learning Engineer specializing in RAG and NLP systems
“Built a 24/7 Python/LangChain email agent in production with validation, circuit breakers, human-review escalation, and structured observability. Also applied data and automation skills at Community Dreams Foundation, including turning a vague donor-insights request into a usable donor-risk prediction workflow and raising ETL reliability from roughly 85% to 99% by diagnosing SQLite concurrency issues.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Rui Zhao
Junior Machine Learning Engineer specializing in semantic search and retrieval systems
“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”
Rathi Anand
Senior Full-Stack Software Engineer specializing in Insurance, FinTech, and AI/ML applications
“AI/backend engineer who fine-tuned and deployed a production LLM chatbot using a LangChain + FAISS RAG pipeline, improving latency with PEFT/LoRA and driving strong business impact (40% customer adoption; 92% satisfaction). Also served as technical lead on a data aggregation system for underwriting/quoting, introducing GraphQL for more efficient, maintainable querying and applying CDC to keep cached ranking data fresh at scale.”
Senior AI/ML Engineer specializing in decentralized AI and cloud-native platforms
Mid-level Machine Learning Engineer specializing in MLOps and production ML systems
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP