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Vetted Recommender Systems Professionals
Pre-screened and vetted.
Junior Data Scientist / AI Engineer specializing in analytics, ML, and MLOps
Mid-level AI Engineer specializing in LLM automation and RAG systems
Mid-level AI/ML Engineer specializing in cloud AI, MLOps, and NLP
Mid-level Data Analyst specializing in marketing analytics and machine learning
Mid-level Applied AI Engineer specializing in LLM agents and RAG systems
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM systems
Senior Full-Stack Engineer specializing in SaaS, LegalTech, and Web3
Swarag Reddy Pingili
Junior AI/ML Software Engineer specializing in LLM agents and RAG systems
“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”
Vikesh Patel
Senior AI/ML Engineer & Data Scientist specializing in LLMs, RAG, and MLOps
“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”
Adithya Sharma
Mid-level AI/ML Engineer specializing in MLOps, NLP, and Generative AI
“Built and deployed a production LLM-powered text-to-SQL/document intelligence chatbot on AWS that lets non-technical business users query complex enterprise databases in plain English. Demonstrates deep practical expertise in schema-aware prompting, embeddings-based schema retrieval, SQL safety/validation guardrails, and rigorous offline/online evaluation with human-in-the-loop approvals for risky queries.”
Lakshmi Swathi Sreedhar
Mid-level AI Engineer specializing in Generative AI and LLM systems
“Built and deployed a production-grade, multi-agent Text-to-SQL assistant that lets non-technical stakeholders query large enterprise databases in natural language. Uses Pinecone-based schema retrieval + LLM reasoning (Gemini/Claude/GPT) with a dedicated validation agent (schema/syntax checks and safe dry runs) to reduce hallucinations and improve reliability, while optimizing latency and cost via async execution and embedding caching.”
Radhika Mangroliya
Senior Data Scientist / AI Engineer specializing in LLMs, RAG, and production ML
“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”
Viswanath Jagaluri
Mid-level Full-Stack & AI Engineer specializing in LLM applications
“Full-stack engineer who has shipped and operated generative-AI chat/QA features end-to-end, including a RAG-based pipeline with guardrails and cost/latency monitoring in production. Experienced with React/TypeScript + Node/Postgres architectures, Dockerized deployments to AWS (EC2) via GitHub Actions CI/CD, and building reliable ingestion/ETL systems with idempotency, backfills, and reconciliation.”
Karthik Patralapati
Mid-level AI/ML Software Engineer specializing in GPU-optimized LLM inference and cloud microservices
“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”
Karan Baid
Intern Machine Learning Engineer specializing in Generative AI and RAG systems
“Early-career AI/LLM builder who created and deployed a multi-agent news analysis agent (Patrakarita) using CrewAI, coordinating researcher/analyst roles to turn noisy article URLs into structured, prioritized outputs (claims, tone, verification questions, opposing views). Strong focus on orchestration debugging and reliability evaluation, including measuring hallucination/redundancy and improving reasoning by refactoring pipeline sequencing.”
Srikar Tharala
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
“Currently at ProShare and reports building an AI/LLM-powered system deployed to production, aimed at helping with status-related difficulties and reducing misunderstandings across transactions. Also cites prior collaboration at Porsche with marketing teams, focusing on translating marketing goals into technical requirements and communicating solutions clearly to non-technical stakeholders.”
Shram Kadia
Junior Software Engineer specializing in ML, RAG systems, and safety-critical risk modeling
“Backend/cloud engineer from Resilient Tech with hands-on experience deploying REST APIs and database migrations into a live ERP used by real customers while maintaining 99% uptime. Has debugged intermittent AWS container timeouts down to security group/load balancer misconfigurations, and has extended Python in an ERPNext system to meet GST/e-invoicing compliance requirements with strong customer collaboration.”