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Vetted Generative AI Engineers
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLMs, RAG, and full-stack development
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud-native MLOps
Senior GenAI Engineer specializing in enterprise LLM systems and RAG platforms
Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”
Meghavardhan Ketireddi
Mid-level AI & Machine Learning Engineer specializing in Generative AI and MLOps
“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”
Sai Charan C
Mid-level Generative AI Engineer specializing in LLMs, RAG, and multimodal AI on AWS
“Built and deployed a production RAG-based enterprise document intelligence platform for financial/compliance/operational documents on AWS (Spark/Glue ingestion, embeddings + vector DB, LangChain orchestration, REST APIs on Docker/Kubernetes). Deep hands-on experience orchestrating multi-step and multi-agent LLM workflows (LangChain, LangGraph, CrewAI) with strong focus on grounding, evaluation, observability, and cost/latency optimization, and has partnered closely with non-technical finance/compliance teams to drive adoption.”
Srichandan Kota
Senior Full-Stack AI Engineer specializing in Generative AI and FinTech
“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”
Karthik Gantasala
Mid-level Generative AI Engineer specializing in LLM agents and RAG
“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”
Pranav Marla
Mid-level AI/ML & Full-Stack Engineer specializing in LLM agents and generative AI
“LLM/agent builder who shipped a live consumer AI-agent app (kalpa.chat) that visualizes complex reasoning as interactive graphs and abstracts multi-provider model usage via a unified wallet. Professionally has applied LangChain/LangGraph to IVR parsing and to scaling a football video-generation pipeline at DAZN, including shipping a VAR-specific retrieval/order fix via SQL after iterating with a non-technical PM.”
Gayatri Devi Dasari
Mid-level GenAI/ML Engineer specializing in LLM systems and RAG chatbots
“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”
Mid-level Prompt Engineer specializing in Generative AI and RAG systems
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs, RAG, and multimodal AI
Mid-level AI/ML & Generative AI Engineer specializing in LLMs and MLOps
Mid-level AI Engineer specializing in LLM apps, RAG pipelines, and agentic workflows
Mid-level Generative AI Engineer specializing in agentic systems and LLM platforms
Junior Machine Learning Engineer specializing in LLMs, Generative AI, and Computer Vision
Senior Data Scientist / GenAI Engineer specializing in RAG, NLP, and MLOps
Mid-level Data Scientist/ML Engineer specializing in Generative AI, NLP, and RAG systems
Mid-level Generative AI Engineer specializing in agentic AI and RAG systems
Mid-level Generative AI Developer specializing in secure RAG and data engineering