Vetted MLflow Professionals

“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).”

Nishitha Thummala

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference

San Francisco, CA6y exp

“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

Screened

Mid-level AI/ML Engineer specializing in LLM fine-tuning, inference optimization, and AI safety

Santa Clara, CA5y exp

“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.”

Nikhil Reddy

Screened

Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms

San Francisco, CA5y exp

“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

Screened

Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants

New York, NY6y exp

“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.”

“Technology-first venture builder with strong familiarity in the VC/accelerator landscape, specializing in greenfield innovation, M&A, and large-scale transformation/modernization. Described building a venture-funded retail banking greenfield startup to integrate lending-as-a-service for SME lending while meeting federal and local financial services compliance requirements.”

“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”

“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.”

“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.”

kartik kanotra

Screened

Mid-level Software Developer specializing in cloud data engineering and MLOps

NYC, New York3y exp

“Software engineer with strong AWS production experience, including an end-to-end historical backfill system exporting ~10PB of CloudWatch logs into a data lake using Step Functions/Kinesis/Lambda/Firehose/Glue. Emphasizes reliability and operability (DynamoDB checkpointing, monitoring dashboards, CI/CD with canary tests) and has also built customer-facing UI work for the Visa Developer Portal using Angular + Spring Boot, plus React/Redux frontend work.”