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Vetted Prompt Engineering Professionals
Pre-screened and vetted.
“ML/LLM engineer with production experience building a RAG-based LLM support assistant (FastAPI, Redis, Kafka) with multi-layer validation and human-in-the-loop feedback loops to improve accuracy over time. Has orchestration and MLOps depth using Airflow and Kubeflow on Kubernetes (autoscaling, alerting, monitoring) and delivered measurable ops impact (40% ticket efficiency improvement) by partnering closely with customer support teams.”
Shanmukha Jwalith Kristam
Mid-level Data Scientist / ML Engineer specializing in MLOps and Generative AI
“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”
“ML engineer/data scientist who deployed a production credit risk + insurance claims triage platform at Hartford Financial, combining XGBoost default prediction with BERT-based document classification. Demonstrated strong MLOps by cutting inference latency to sub-500ms and building drift monitoring plus automated retraining/deployment pipelines (MLflow, CloudWatch, GitHub Actions, SageMaker) with human-in-the-loop review and SHAP-based explainability for underwriting adoption.”
Rahul Mangalampalli
Mid-level AI Software Engineer specializing in computer vision and multimodal systems
“Robotics/perception engineer focused on production-grade, real-time systems—optimized self-supervised segmentation on Jetson Nano from ~6–10 FPS to ~20–25 FPS and scaled experimentation/deployment by unifying 15+ edge models in a modular PyTorch Lightning framework. Experienced integrating distributed LiDAR-camera fusion via gRPC/protobuf into mission planning, migrating ROS1→ROS2 Foxy for multi-drone perception, and adding Prometheus-based observability for long-running deployments.”
Shreyansh Bhalani
Mid-level Full-Stack & ML Engineer specializing in AI SaaS, MLOps, and cloud infrastructure
“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”
Sai Nikhitha Chowdary Manne
Junior Software Developer specializing in AI/LLM agent systems
“Built an LLM-powered agent within the Nora AI analytics platform to automate e-commerce product performance analysis and generate actionable recommendations (pricing/inventory), designed with production-grade reliability patterns and observability. Emphasizes predictable, schema-validated tool/function-calling pipelines with robust fallbacks, idempotency, and guardrails for messy operational data.”
Yashi Agarwal
Mid-level Machine Learning Engineer specializing in NLP, Generative AI, and RAG systems
“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”
SREEJA REDDY Konda
Mid-level AI/ML Engineer specializing in NLP, MLOps, and predictive analytics
“AI/ML Engineer at Fifth Third Bank who has shipped production fraud detection and risk analysis systems combining ML models with LLM-powered insights/explanations, including real-time monitoring, drift detection, and automated retraining under regulatory explainability constraints. Also built a hybrid-retrieval internal knowledge-base QA system (+20% top-5 relevance) and delivered a customer support chatbot that reduced first response time by 30% through strong stakeholder collaboration.”
sai Pavan
Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines
“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”
Teja Babu Mandaloju
Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms
“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”
Sai Bandaru
Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems
“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”
Uttam Kumar
Intern AI Engineer specializing in LLM agents, RAG, and scalable cloud deployment
“AI/LLM engineer at GPT integrators who built a production multi-agent enterprise workflow integration system, tackling hard problems in agent orchestration, layered memory, and custom RAG over enterprise/user data. Also built an education-focused agent solution integrating with Canvas, Zoom, and email to automate classroom admin tasks, and is currently applying agentic AI to insurance underwriting workflows in collaboration with underwriters.”
Sai Erramada
Mid-level Full-Stack Java Developer specializing in microservices and cloud-native systems
“Backend engineer with hands-on experience building real-time, event-driven systems at Walgreens, including a Kafka-based prescription status notification service and scalable pipelines for messy prescription/inventory data. Strong focus on reliability patterns (retries, idempotency, DLQs) and iterating based on pharmacist feedback to improve usability.”
Rahul Ganesan
Intern AI Engineer specializing in LLM systems, RAG, and cloud data pipelines
“Built and deployed a production Dockerized multimodal (voice+text) LLM agent for knowledge management that retrieves from Notion and documents and falls back to Tavily-powered web search with citations when internal notes are missing. Emphasizes production reliability via model-switching fallbacks, caching, strict structured outputs (Pydantic/JSON schema), and MCP-based orchestration with state-aware gating and monitoring to reduce redundant tool calls and improve success rates.”
Abhishek Basu
Junior Backend Software Engineer specializing in cloud and AI systems
“Built and shipped LLM-enabled decision systems focused on real production reliability rather than chatbot demos, including a multimodal radiology retrieval platform with 28% relevance gains and 35% lower latency. Also architected a 4-agent employee analytics workflow with structured outputs, traceable orchestration, and strong safeguards for messy real-world data.”
Antoine Andre
Senior Account Executive specializing in AI/SaaS and technical B2B sales
“Founding recruiter/seller at Remax who built the entire outbound engine from zero—cadences, CRM workflows, automations, Chrome extensions, discovery framework, and ROI templates—without SDRs, inbound, or an existing playbook. Combines consultative selling with technical process-building, and turned a cold-sourced broker into a 5-person team producing $14M in annual volume while reducing meeting conversion timelines by 40%.”
Anvith Reddy T
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”
Pranathi Nallala
Mid-level AI Engineer specializing in distributed systems and LLM applications
“Built production AI agents that convert natural-language requests into structured workflows using LangChain, tool calling, and a Kafka/Kubernetes backend, with strong emphasis on tracing, validation, and self-correcting failure handling. Also drove a zero-to-one Research Day judging platform spanning React, Flask, RAG, and ILP-based assignment optimization for ~100 live posters, achieving 99% uptime and winning Best Web App.”
Rohan Chodapunedi
Entry-level Data Scientist specializing in LLMs and analytics
“Built a zero-to-one AI contract/policy QA agent for compliance and data teams, with a strong emphasis on trust, traceability, and clause-level citations rather than just fluent answers. They combine full-stack product ownership with practical LLM systems design, including hybrid retrieval, structured outputs, and evaluation pipelines to improve reliability, latency, and cost.”
Humera Naaz
Mid-level Full-Stack Developer specializing in cloud-native enterprise applications
“Engineer with hands-on experience embedding AI into software delivery workflows, including Claude-powered PR review, testing, debugging, and multi-agent coding pipelines. They pair AI automation with strong systems thinking around microservices, fault tolerance, multi-AZ design, caching, and security controls like WAF and rate limiting, and also experiment independently with RAG and multi-agent search projects.”
Sakshi Patel
Mid-level Software Engineer specializing in e-commerce and supply chain platforms
“AI-focused developer who has built several practical AI products, including EchoMate, a voice-agent system designed to act as a proxy for doctors and support patients when physicians are unavailable. Also has experience with multi-agent/API-based workflows in a solar suitability project, showing interest in applying AI across both healthcare and climate-related use cases.”
George Platon
Principal Full-Stack Engineer specializing in AI, DevOps, and cloud platforms
“Built a production end-to-end AI video-to-reels clip extraction system using a multi-agent architecture with transcription, captioning, effects generation, and centralized orchestration. Demonstrates unusually strong systems thinking around reliability, observability, evaluation, and production tradeoffs for LLM-powered workflows, including Kubernetes/Kafka-based deployment and regression-driven prompt governance.”
Atharva Rajesh Patil
Junior Software Engineer specializing in backend systems and cloud-native applications
“Engineer with hands-on experience owning customer deployments for ordering and billing systems at Amdocs, including performance tuning, CI/CD improvements, and post-launch stabilization that delivered about 50% faster execution time. Also built and debugged an LLM-powered task prioritization app using Gemini, Streamlit, Python, and MongoDB, with a strong focus on prompt reliability, validation, and handling inconsistent real-world inputs.”
Kevin Delong
Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”