Vetted Embeddings Professionals

“Built a production internal LLM-powered knowledge assistant using a RAG architecture (Python, LLM APIs, cloud services) that answers employee questions with sourced, grounded responses from internal documents. Demonstrates strong practical depth in retrieval tuning (chunking/metadata filters), orchestration with LangChain, and production reliability practices (latency optimization, automated embedding refresh, evaluation metrics, logging/monitoring) while partnering closely with non-technical operations teams.”

Bryan West

Screened

Senior Software Engineer specializing in AI, cloud infrastructure, and full-stack development

Chantilly, VA17y exp

“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”

Sai Vamsi

Screened

Mid-Level Software Engineer specializing in backend microservices and cloud-native systems

TX, USA4y exp

“ServiceNow engineer who built an AI-powered ticket summarizer end-to-end (RAG with vector DB + GPT, Redis latency optimizations, fallback summarization, and a React UI widget for agent feedback). Also has hands-on ROS 2 experience building real-time sensor-fusion nodes (LiDAR/IMU), debugging SLAM/navigation issues via rosbag + EKF tuning, and bridging heterogeneous robots by translating ROS 2 topics to MQTT/JSON. Strong DevOps background with Docker, Jenkins CI/CD, and Kubernetes orchestration for scalable deployments.”

Preetham Reddy Konuganti

Screened

Junior Full-Stack Engineer specializing in AI applications and scalable web platforms

San Jose, CA2y exp

“Full-stack engineer with customer-facing delivery experience who built and deployed a multi-platform social media automation product (Next.js/Node/MongoDB) and optimized it using BullMQ/Redis background jobs, retries, and rate limiting for reliable posting at scale. Also delivered an AI-powered false-positive analysis service in a cybersecurity context, resolving production pipeline stalls via log-driven debugging, parallelization, caching, and LLM guardrails.”

SAITEJA MALLEMPUDI

Screened

Senior Data Scientist and AI/ML Engineer specializing in GenAI and cloud ML

Chicago, IL6y exp

“ML/AI engineer with hands-on experience owning systems from experimentation through deployment and monitoring, including a Bank of Montreal project that improved timely interventions by 12%. Also brings GenAI/RAG experience with evaluation and safety guardrails, plus clinical NLP pipeline work extracting medication data from notes for patient risk prediction.”

“Backend/AI infrastructure engineer focused on production-ready LLM systems and distributed workflows. They described building a RAG-based multi-step agent with strong reliability controls, evaluation loops, and graceful degradation that improved latency by 30%, retrieval accuracy by 15%, and reduced support workload by 40%.”

Naveena Musku

Screened

Mid-level AI/ML Engineer specializing in agentic AI and LLM systems

5y exp

“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”

“AI/full-stack product engineer with experience shipping agentic systems in both fintech and enterprise compliance contexts, including an Anthropic-powered compliance Q&A tool at American Express and a home-equity assessment experience for seniors. Stands out for combining strong product instincts, typed full-stack implementation, and rigorous LLM evaluation/monitoring practices to improve trust, adoption, and operational efficiency.”

“Built an agentic medical coding system at Optum that combined LangGraph, LangChain RAG, Azure OpenAI, pgvector, and TypeScript to automate routine clinical coding while escalating risky cases to humans. The system automated about 40% of routine cases at roughly 92% accuracy, with strong production evals and observability using MLflow, Ragas, and DeepEval.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“ML practitioner with hands-on experience adapting diffusion models (DDPM + U-Net in PyTorch) to improve low-dose CT medical imaging quality via denoising and upsampling against high-dose ground truth. Also built a RAG workflow during a recent internship by cleaning client survey data, embedding with OpenAI text-embedding-3-large, and indexing in Pinecone with MD5 deduplication, alongside a strong emphasis on production-grade Python practices.”

“Backend/AI workflow engineer with experience at AirKitchenz, Uber, and Vivma Software, building production systems on AWS (Lambda, DynamoDB, Step Functions). Has a track record of major performance wins (DynamoDB latency 2s to <150ms; Postgres query 2s to ~180ms) and shipping LLM-powered onboarding and ticket-routing workflows with strong guardrails (schema validation, confidence thresholds, human-in-the-loop escalation).”

“Full-stack Java engineer (4+ years) who led end-to-end modernization of high-latency order management systems into cloud-native reactive microservices (Spring WebFlux) and built real-time React/Redux dashboards, reporting 99.98% uptime and 22% infra cost savings. Also headed a production RAG-based Order Support Bot at Dell Technologies with embeddings + MongoDB semantic search, automated validation and human fallback, plus CI/CD-driven LLM eval loops to reduce hallucinations.”

“Built and shipped production systems spanning real-time operational dashboards and an LLM-powered internal documentation assistant using RAG (embeddings + vector DB). Demonstrates strong focus on reliability and iteration: implemented guardrails and evaluation loops (human review, hallucination tracking, regression prevention) and improved performance/scalability through query optimization, caching, and retrieval tuning.”

“Built an autonomous decision analytics LLM agent for end-to-end tabular binary classification, using RAG (FAISS) to retain context across multi-step queries. Deployed as a FastAPI service with production-style reliability features (schema-aware validation, fallbacks, retries, structured outputs) plus offline/online evaluation and monitoring to reduce analysis time and improve consistency versus stateless approaches.”

Saikrishna Vallala

Screened

Mid-level QA Automation Engineer / SDET specializing in Financial Services and Healthcare

USA5y exp

“Fintech-focused engineer who built an end-to-end KYC verification pipeline for advisor onboarding using Flask microservices, Celery/Redis, and AWS (Lambda/ECS/EC2) with CloudWatch-driven scaling and latency optimizations. Also shipped a production internal knowledge assistant using RAG + embeddings/vector search with guardrails (similarity-based fallback, prompt-injection protections) and an evaluation loop with compliance specialist review that drove measurable retrieval improvements.”

Aniket Janrao

Screened

Junior Data Scientist specializing in healthcare ML and clinical NLP/LLMs

Houma, LA2y exp

“Healthcare-focused LLM engineer who has built two production clinical applications: an automated structured clinical report generator from physician-patient conversations and a RAG-based chatbot for retrieving patient history (procedures, allergies, etc.). Demonstrates strong applied RAG expertise (overlapping chunking, entity dependency graphs, temporal filtering, graph RAG) to reduce hallucinations/omissions and partners closely with clinicians to automate hospital workflows.”

“Built and deployed a GenAI-powered PhD career intelligence platform at NYU that maps academic backgrounds to career paths and converts long academic CVs into job-ready resumes. Stands out for treating LLM systems as structured production pipelines—combining NLP extraction, embeddings, orchestration, and AWS deployment—to improve recommendation quality and cut resume preparation time by 70%.”

“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”

“Built production AI systems in high-stakes domains, including a medical RAG chatbot focused on reducing hallucinations and a document-processing workflow that automated manual PDF extraction. Demonstrates strong end-to-end ownership across backend services, APIs, LLM integration, and iterative reliability improvements based on real usage and failure analysis.”

“Growth-focused B2B SaaS operator with hands-on experience improving enterprise adoption for a cloud governance and FinOps platform. They combine customer discovery, ROI-driven messaging, automation, and funnel instrumentation to improve conversion and handoffs, citing an 18% lift in enterprise adoption and roughly $200K-$3M in influenced pipeline.”

“Full-stack/product-minded engineer with strong React/TypeScript depth who has owned systems end-to-end, from UI architecture to backend services and data design. At Qualcomm, they built both a telemetry dashboard and an ML model drift monitoring platform for 20+ edge models, including post-launch tuning that cut false positives by 60%. They also demonstrate 0→1 startup execution by solo-building a production RAG document Q&A platform with JWT auth, Stripe gating, and sub-300ms retrieval.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“ML/LLM practitioner who has shipped production RAG systems (summarization + Q&A) and end-to-end Airflow-orchestrated demand forecasting pipelines at NEON IT. Strong focus on reliability—uses evaluation scripts, retrieval/chunking tuning, validation/retries/alerts, and stakeholder-driven iteration to make AI workflows consistent and usable.”