Vetted AI & Machine Learning Professionals

“Robotics software engineer (master’s student) who placed 3rd in the CMU VLA challenge and presented at IROS, building an LLM-powered language system (Gemini 2.5) for mobile-robot scene Q&A and language-based navigation. Hands-on ROS1/ROS2 experience including ros2_control + PILZ planning for a KUKA arm, plus simulation (Gazebo) and containerized submissions with Docker.”

“Backend/infra-focused engineer with experience spanning Go-based MCP servers for an AI-assisted Kubernetes on-call diagnosis chatbot and a Python/Flask PagerDuty automation integration. Previously at Tesla, optimized high-volume battery test data in PostgreSQL using JSONB, partitioning, and a timestamp normalization pipeline; also built PyTorch PINN training workflows and achieved a 20x speedup via batch vectorization.”

“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).”

“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”

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.”