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Vetted Plotly Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in Generative AI, LLMs, and Computer Vision
Mid-level Data Scientist specializing in ML, NLP and forecasting
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices
Mid-level Software Engineer specializing in AI/GenAI and cloud-native backend systems
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Junior Data Scientist specializing in machine learning and recommendation systems
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
Junior Generative AI Engineer specializing in LLM fine-tuning and RAG pipelines
Mid-level AI Engineer specializing in NLP, LLM fine-tuning, and RAG systems
Mid-level Software Engineer specializing in full-stack data systems and cloud automation
Mid-level Full-Stack Software Engineer specializing in cloud-native AI and enterprise platforms
Junior Data Scientist / AI Engineer specializing in analytics, ML, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-Level Machine Learning Engineer specializing in LLMs and RAG systems
Mid-level AI/ML Engineer specializing in MLOps, streaming data, and NLP/CV
Senior Backend/Infrastructure Engineer specializing in Python microservices and AWS
Senior Full-Stack Engineer specializing in growth, analytics, and funnel optimization
Maneesh Ujji
Junior Machine Learning & Data Science professional specializing in AI agents and applied ML
“IT Analyst/research background with hands-on experience deploying and hardening a multi-agent AI support/triage system (ticket ingestion + knowledge-base retrieval) with strong emphasis on reliability and observability. Has debugged real production issues spanning backend services and network latency (sync failures/partial writes) and is comfortable in Linux environments; also has academic exposure to robotics simulation and ROS2.”
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.”
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.”
Ofek Shaltiel
Mid-Level Full-Stack Software Engineer specializing in AI-enabled web platforms
“Backend/AI engineer in construction tech (HyperWater AI) who delivered major production performance wins (analytics API from ~1 hour to 0.5s) and shipped LLM features for parsing subcontractor manifests into CSI divisions with human-in-the-loop review. Also built a freelance agentic document-verification system using OCR + RAG over pgvector with robust retry/escalation logic and user feedback loops.”
Ram Abhinav Vedant Madabushi
Junior Full-Stack/AI Engineer specializing in web platforms and LLM applications
“Backend engineer from FoodSupply.ai who built and evolved a scalable restaurant/supplier product and order management platform using Node.js and REST APIs. Implemented a hybrid MySQL+MongoDB data architecture, optimized performance with Redis/Prisma, and led a phased migration with feature flags and a temporary sync layer to maintain data consistency. Strong focus on production security (OAuth2, RBAC, row-level security, AWS IAM) and reliability practices (testing with Pytest, Docker/AWS pipelines).”
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.”