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Vetted MLflow Professionals
Pre-screened and vetted.
Mid-level Data Engineer specializing in cloud data platforms for Healthcare and Financial Services
Mid-level Data Engineer specializing in cloud lakehouse and real-time streaming
Intern-level quantitative software engineer specializing in FinTech and trading systems
Senior Machine Learning Engineer specializing in Generative AI and LLM systems
Mid AI/ML Engineer specializing in MLOps, deep learning, and cloud ML systems
Senior Data Scientist and AI Engineer specializing in NLP, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Mid-level Software Engineer specializing in full-stack AI and cloud-native systems
Senior AI Architect specializing in Generative AI and LLM systems
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
Director-level Engineering Leader specializing in cloud platforms, AI/ML, and scalable SaaS
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
Principal Automation Architect specializing in cloud DevOps, microservices, and MLOps
Senior AI Python Engineer specializing in Generative AI and MLOps
Pravallika Kilari
Mid-level AI/ML Engineer specializing in NLP, GenAI, and MLOps in healthcare and finance
“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
“Built an LLM-powered academic research assistant for a professor (LangChain + OpenAI + arXiv) focused on synthesizing papers quickly, with emphasis on reliability (ReAct prompting, citation verification) and cost control (caching). Has production MLOps/orchestration experience at Cisco and HCL Tech using Kubernetes, plus MLflow and GitHub Actions for lifecycle management and CI/CD.”