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Vetted MLflow Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Principal Data Scientist specializing in ML, NLP, and forecasting for marketing and supply chain
Principal Machine Learning Scientist specializing in GenAI, LLMs, and RAG
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps
Staff Data Scientist / AI-ML Engineer specializing in fraud detection, NLP, and recommendations
Staff Full-Stack Engineer specializing in data engineering and real-time event platforms
Senior Full-Stack Software Engineer specializing in Telehealth and FinTech
Senior Machine Learning Engineer specializing in LLM inference and GPU infrastructure
Mid-Level Software Development Engineer specializing in AWS serverless and ML/GenAI
Mid-level Machine Learning Engineer specializing in generative AI, NLP, and MLOps
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems
Nishitha Thummala
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference
“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”
Kowshika M
Mid-level AI/ML Engineer specializing in LLM fine-tuning, inference optimization, and AI safety
“AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.”
Kenil Tanna
Staff-level Machine Learning Engineer specializing in LLMs and MLOps for Financial Services
“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”
Nikhil Reddy
Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms
“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”
Krishna Reddy
Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants
“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”
Krishna Sahith Poruri
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”