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Vetted AI Engineers
Pre-screened and vetted.
Mid-level AI Engineer specializing in LLM agents, RAG, and production automation
Junior Machine Learning Engineer specializing in LLM training and high-performance inference
Junior Generative AI Engineer specializing in multi-agent systems and LLM evaluation
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time AI systems
Mid-level AI Engineer specializing in LLM agents, RAG, and MLOps for financial services
Mid-level AI Engineer & Data Scientist specializing in Generative AI, NLP, and Cloud ML
Senior Full-Stack AI Engineer specializing in AI products and developer platforms
Senior AI Architect specializing in Generative AI and LLM systems
Saikrishna Paila
Junior AI Engineer specializing in RAG pipelines and agentic AI systems
“Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.”
Vinay Sanagapalli
Mid-level AI Engineer specializing in LLM agents and RAG systems
“AI/ML engineer at MRI Software focused on taking LLM and RAG systems from prototype to reliable production. Notable work includes an AI automation system for migrating 1200+ legacy pages with 75-80% manual effort reduction, plus enterprise document-querying and reusable Python LLM infrastructure that cut lookup time by 70% and improved team velocity by 30-40%.”
Devang Borkar
Mid-level AI Engineer specializing in LLM agents and evaluation systems
“Built an end-to-end Python integration for an emotion-aware presentation feedback system that processed uploaded or live video and analyzed facial emotion, tone, and gestures. Also has Playwright automation experience in a loan management workflow, with emphasis on reliability, observability, security, and iterative delivery under ambiguous requirements.”
Yogita Adari
Mid-level AI Engineer specializing in generative and multimodal systems
“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”
Sri Teja
Mid-level AI Engineer specializing in LLM systems and enterprise data platforms
“Built and owned key parts of Ripley, an AI-powered multi-agent operations platform for roadside assistance that automates high-volume customer service workflows at production scale. They designed the orchestration, evaluation, monitoring, and enterprise integrations, helping drive 70-80% automation and ~99% reliability across thousands of weekly interactions and millions of annual requests.”
Harsh Tripathi
Mid-level Machine Learning Engineer specializing in LLMs, agentic AI, and risk/fraud modeling
“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”
Harideep Balusa
Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems
“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”
Devin Jackson
Mid-level Gameplay AI Engineer specializing in Unreal Engine
“UE5 gameplay/system designer with an engineering background who has shipped player-facing systems including an enemy weak-point feature (with replication and performance fixes) and a modular spectator minigame framework for Killer Klowns from Outer Space: The Game. Also implemented lobby mode and disconnect team-balancing (AI backfill) for Ghostbusters: Spirits Unleashed, leveraging profiling/debug tooling and cross-discipline collaboration to get features to shipping quality.”
Ojasmitha Pedirappagari
Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms
“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”
Nikshitha Aella
Mid-level Full-Stack Software Engineer specializing in AI platforms and microservices
“Backend engineer currently building an AWS Lambda/FastAPI inventory recommendation system using a LangChain + GPT-4 RAG pipeline and MongoDB vector search; drove major cost optimization via Redis caching (60% reduction) while sustaining 10k+ daily requests under 2s latency. Previously deployed Node.js microservices on AWS OpenShift with Jenkins/Helm at UnitedHealth Group and led a zero-downtime monolith-to-microservices migration at Verizon, including RabbitMQ-based real-time messaging with DLQs and idempotency.”