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Vetted AI & Machine Learning Professionals in Remote
Pre-screened and vetted in Remote.
Mid-level AI/ML Engineer specializing in NLP/LLMs and real-time data pipelines
Junior Machine Learning Engineer specializing in benchmarking, NLP, and computer vision
Mid-level AI/ML Engineer specializing in NLP, MLOps, and fraud detection
Mid-level AI/ML Engineer specializing in MLOps, NLP, and predictive modeling
Mid-level AI/ML Engineer specializing in recommendations, NLP, computer vision, and MLOps
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps in Financial Services
Mid-level AI/ML Engineer specializing in scalable ML systems and cloud MLOps
Intern UI/UX Engineer specializing in accessible, responsive web interfaces
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Shon Little
Director-level Software Engineering Leader specializing in AI platforms and full-stack cloud systems
“Engineering leader with BCG consulting background who has built roadmaps and scaled AI and data platforms for pharma and manufacturing clients. Led architecture shifts (Django monolith to event-driven microservices) for high-volume IoT SaaS products, improving deployment speed and enabling zero-downtime releases. Also established a near-shore engineering team in São Paulo and has managed distributed teams across multiple countries, leveraging strong stakeholder communication and a prior professional acting background for storytelling.”
Pratik Jaiswal
Mid-level AI/ML Engineer specializing in financial services ML and MLOps
“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and compliance-focused ML systems
Mid-level AI/ML Engineer specializing in NLP, speech AI, and RAG systems
Mid-level AI/ML Engineer specializing in forecasting, MLOps, and generative AI
Anvith Reddy Dodda
Mid-level AI Engineer specializing in GenAI, NLP, and MLOps
“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”
“Built and productionized an AI-native, agentic appeals decisioning system for health insurance operations, automating 500k+ scanned appeals/year. Delivered measurable impact by cutting review time from 12–15 minutes to ~3 minutes and auto-resolving ~85% of cases with strong auditability, evaluations, and human-in-the-loop guardrails, deployed as containerized microservices on Azure AKS.”
Nikita Prasad
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable data pipelines
“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”
Sharath Kumar
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”