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Vetted AI & Machine Learning Professionals in Remote
Pre-screened and vetted in Remote.
Mid-Level Machine Learning Engineer specializing in AI backend and generative AI
Mid-level Machine Learning Engineer specializing in MLOps, streaming analytics, and financial risk
Mid-level AI/ML Engineer specializing in LLMs, RAG, and recommendation systems
Mid-level Machine Learning Engineer specializing in MLOps and production AI
Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps
Mid-level Data Engineer specializing in AWS, real-time pipelines, and ML/GenAI data platforms
Mid-level AI/ML Engineer specializing in NLP, MLOps, and cloud deployment
Entry-level AI engineer and quantitative analyst specializing in finance and data modeling
Mid-level AI/ML Engineer specializing in NLP, recommender systems, and MLOps in financial services
Junior AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable ML platforms
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Mid-level AI/ML Engineer specializing in Generative AI and fraud detection
Sharath Pampalker
Mid-level AI/ML Engineer specializing in real-time anomaly detection and AI agents
“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”
Hsi-Chun Wang
Mid-level Data Scientist specializing in LLM development and scalable ML pipelines
“Built and deployed production LLM pipelines for evidence-based scoring in two domains: biomedical literature mining (scoring ~2700 drug compounds vs gene targets/mechanisms) and long-horizon news analytics (35 years of Chinese articles). Emphasizes reliability at scale (retries/checkpointing/validation), rigorous empirical model benchmarking (GPT-4o/mini/5), and translating results into stakeholder-friendly visual narratives.”
Ponaganti Sanjana
Mid-level AI/ML Engineer specializing in NLP, MLOps, and FinTech
“ML/AI engineer with production experience at S&P Global and Accenture, focused on regulated, enterprise-grade systems. Built end-to-end financial risk and credit default models with >90% precision and 12% fewer false positives, and is currently developing secure RAG pipelines on AWS SageMaker for enterprise insight extraction.”
Nasser Ben
Junior Full-Stack Software Engineer specializing in AI/ML and LLM integration
“Built a personal product, Pilly AI—an AI-powered e-commerce product Q&A widget embedded via a simple script tag and served via Cloudflare CDN—covering landing page, backend, database, and deployment end-to-end. Implemented OpenAI integration with prompt/context engineering, JWT-authenticated APIs, and Postgres (NeonDB), and successfully sold the product to a client while shipping in roughly two weeks.”
Kranthi Kumar Karupati
Mid-level Generative AI Engineer specializing in LLM apps, RAG, and MLOps
“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”
jagadeesh Meruva
Mid-level AI Engineer specializing in Generative AI and healthcare search
“AI and platform engineer with 5 years of experience who built a production knowledge assistant for Verizon end-to-end, from architecture through deployment, monitoring, and incident hardening. Stands out for combining modern LLM/RAG systems with enterprise-grade rigor, including validation layers, observability, versioning safeguards, and measurable impact on technician productivity and retrieval quality.”