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Vetted Prometheus Professionals
Pre-screened and vetted.
Mid-level Backend Software Engineer specializing in AI-powered microservices and cloud infrastructure
Senior DevSecOps Engineer specializing in secure CI/CD and cloud compliance
Senior Backend/Infrastructure Engineer specializing in Python microservices and AWS
Senior Java Full-Stack Developer specializing in microservices and cloud (AWS)
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM systems
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and SPAs
Mid-level Full-Stack AI Engineer specializing in agentic RAG and LLM fine-tuning
Senior DevOps & AWS Cloud Engineer specializing in scalable, secure cloud infrastructure
Mid-level Full-Stack Software Engineer specializing in AI-powered document platforms
Junior Full-Stack Engineer specializing in mobile apps and backend systems
Mid-level Full-Stack Software Engineer specializing in web applications and cybersecurity platforms
Senior Python Backend Engineer specializing in scalable APIs, cloud microservices, and AI/ML platforms
Senior DevSecOps Engineer specializing in AWS GovCloud, Kubernetes, and compliance automation
Senior Cloud & DevOps Engineer specializing in AWS, Azure, and GCP automation
Principal Infrastructure Engineer specializing in distributed systems, cryptography, and hardware security
Naveen Kancharla
Mid-level AI & Backend Engineer specializing in RAG systems and scalable APIs
“Built and deployed a production LLM-powered document Q&A system using a strict RAG pipeline (LangChain-style orchestration + FAISS) to help users query large internal document sets. Demonstrates strong reliability focus through hallucination mitigation, curated offline evaluation with grounding checks, and production monitoring (latency/fallback rates) plus stakeholder alignment via demos and business metrics.”
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.”
Saivedant Hava
Entry AI Engineer specializing in LLMs, RAG, and MLOps
“Built and shipped a production Python-based agentic RAG document retrieval system over 80K records using FastAPI, OCR, vector search, and AWS infrastructure, with a strong emphasis on reliability, testing, and observability. Stands out for treating AI failures like production incidents—turning hallucinations, retrieval misses, and OCR issues into regression tests—and for quantifiably reducing document lookup time from about 12 minutes to under 90 seconds.”
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.”
Ronald Forte
Entry-Level Software Engineer specializing in AI APIs and RAG systems
“Junior/entry-level AI/LLM engineer who built a production-oriented RAG onboarding and knowledge assistant that ingests GitHub repos and internal sources (e.g., Confluence/Jira) using ChromaDB, with reliability features like retrieval fallbacks, retries, caching, and monitoring. Currently implementing a LangGraph-based multi-agent workflow with intent routing and Pydantic/Magentic-validated structured outputs, plus CI/CD offline evals and online metrics (Grafana/Prometheus) to improve predictability and reliability.”