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Vetted Data Scientists
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in scalable ML systems and cloud MLOps
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and MLOps
Mid-level Data Scientist / GenAI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Data Scientist specializing in customer analytics, ML pipelines, and churn forecasting
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Mid-level AI Data Scientist specializing in financial risk, fraud detection, and NLP/LLM systems
Mid-level AI/ML Engineer and Developer Educator specializing in GenAI, RAG, and AI community building
Mid-level AI/ML Engineer specializing in MLOps, distributed ML, and RAG pipelines
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
Senior Data Scientist specializing in Generative AI, NLP, and MLOps
Senior AI Platform Engineer specializing in agentic AI and RAG systems
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.”
Manish Mallikarjuna Devatha
Principal Applied Scientist specializing in ML systems and Generative AI
“Built and owned an end-to-end agentic RAG chatbot platform for Baptist Health that helped clinicians access policy and clinical documents faster, reducing manual lookup by 80% and delivering about $2M in annual savings. Brings strong healthcare GenAI production experience, including HIPAA-aligned governance, PHI redaction, observability, evaluation, and scalable Python/Kubernetes deployment practices.”
“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.”
“ML/LLM practitioner with experience at Truveta building an LLM-based evaluation framework; identified non-overlapping evaluator failure modes and proposed an ensemble approach that enabled scaling training data and drove ~5% performance gains across multiple internal projects. Strong focus on robustness to distribution shift (augmentation/domain adaptation/meta-learning) and production reliability via monitoring, drift detection, and safe fallbacks.”
Hachem Hamadeh
Mid-level Applied AI Engineer specializing in ML systems, MLOps, and industrial analytics
“Industrial AI/ML practitioner with experience deploying real-time monitoring and anomaly detection in a regulated Sanofi vaccine manufacturing facility, including root-cause workflows, logging/alerting, and SOP-aligned validation—achieving ~90% faster anomaly detection. Also built Python/NLP-style automation to accelerate instrumentation & control documentation (~40% faster) and delivered end-to-end predictive analytics for an agri-food operations/distribution client using close operator and leadership feedback loops.”
Haider Shah
Principal AI/ML Architect specializing in GenAI, LLMs, RAG, and Agentic AI
“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”