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Vetted Llama Professionals
Pre-screened and vetted.
Mid-level Data Analyst specializing in ML, AI, and data visualization
Mid-level Data Scientist specializing in ML, NLP, and cloud deployment
Senior Unity/XR Engineer specializing in OpenXR mixed reality and real-time rendering
Senior Software Engineer specializing in GenAI and full-stack enterprise applications
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior AI Architect specializing in Generative AI and LLM systems
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
Harshini Kanamathareddy
Mid Software Engineer specializing in iOS, backend systems, and AI-powered applications
“Full-stack/backend engineer with experience spanning React/TypeScript, Flask, Spring Boot, SQL databases, and production mobile optimization. They’ve shipped features end to end, improved query performance and app startup/crash metrics, and helped drive a configuration-driven architecture that enabled faster releases across 30 consumer applications.”
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.”
GOWRI SHANKAR ANANTHULA
Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG
“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”
Ruudra Patel
Junior Data Scientist specializing in ML, LLMs, and RAG applications
“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”
Anvesh Reddy Narra
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”
Mansoor Khan
Mid-level Conversational AI Developer specializing in enterprise chatbots and RAG
“ML/AI practitioner with hands-on experience deploying models to production and optimizing for low-latency inference using pruning/quantization, with deployments on AWS SageMaker and Azure ML. Has orchestrated end-to-end ML pipelines with Airflow and Kubeflow (ingestion through evaluation) and emphasizes reproducibility via containerization and version-controlled artifacts, while effectively partnering with non-technical stakeholders using dashboards and business-aligned metrics.”
Apurva Banka
Mid-level Full-Stack & AI Engineer specializing in cloud, data platforms, and LLM automation
“Software engineer/product builder who has owned an agentic affiliate lead-gen platform end-to-end (Django + React/TypeScript) and deployed it on Kubernetes in anticipation of 10x user growth from ~5K DAUs. Also has healthcare claims microservices experience using Kafka, including hands-on performance tuning to address consumer lag and broker pressure, and built an internal downtime alerting tool adopted across the organization.”
“ML/NLP engineer with recent Scotiabank experience building production-grade indexing automation over large-scale emails and customer databases, combining LLM fine-tuning (Mistral, XLM-R) with fuzzy matching to exceed 95% accuracy under strict banking constraints. Also built a RAG-based chat agent using Gecko embeddings, Vertex AI Search, Gemini, and cross-encoder reranking, and delivered a text-to-SQL chatbot at SOTI through iterative fine-tuning and benchmark-driven experimentation.”
Phani K
Mid-level AI/ML Engineer specializing in GenAI, NLP, and healthcare-financial ML
“ML/AI engineer with hands-on experience shipping healthcare AI systems, including an oncology risk prediction platform and RAG-based clinical decision support tools. Stands out for combining clinical domain context with strong production engineering across Spark, FastAPI, AWS SageMaker, monitoring, evaluation, and safety guardrails.”
Raghvesh Prasad
Senior Full-Stack Engineer specializing in AI, backend systems, and supply chain platforms
“Full-stack engineer with hands-on experience spanning React/TypeScript frontends, Cloudflare serverless RAG systems, SQL-heavy backend redesigns, and computer vision workflows. He has shipped practical automation and reliability improvements with measurable impact, including cutting a video-validation reporting process from a week to 2 days and fixing a memory-heavy shipment system before Black Friday to support 30K+ orders successfully.”
Raj Kalantri
Intern-level Software Engineer specializing in AI/ML and full-stack development
“Built a sophisticated AI career counselor as a full-stack web app for early-career students, integrating React, Flask, Pinecone, and LLM inference into a stateful conversational product. Stands out for combining hands-on debugging of retrieval/embedding pipelines with strong browser-performance instincts and pragmatic UX iteration based on real user testing.”
Vikash Mediboina
Mid-level Machine Learning & Full-Stack Engineer specializing in GenAI platforms
“LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.”
Muaaz Syed
Mid-level AI/ML Engineer specializing in NLP and conversational AI
“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”
Harideep Balusa
Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems
“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”