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Vetted AI Engineers
Pre-screened and vetted.
Param Yanamandra
Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation
“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”
Srikar Gundreddy
Mid-level Software Engineer specializing in Robotics and AI systems
“Software Developer at Amazon Robotics who co-developed a congestion-aware path planning system optimizing robot routes across 23 warehouses. Built and operated a real-time, service-integrated pipeline using AWS (AppConfig, DynamoDB), Java, and Redis caching, and has hands-on experience debugging robot behavior on-site with rigorous testing and staged releases.”
Praveen V
Mid-Level Software Engineer specializing in Generative AI and RAG systems
“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”
Mid-level AI Engineer specializing in computer vision and RAG systems
Mid-level Machine Learning Engineer specializing in LLM inference and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Junior AI Engineer specializing in LLM agents and RAG for energy operations
Junior AI/ML Engineer specializing in LLMs, RAG, and document intelligence
Senior AI Infrastructure & Backend Engineer specializing in LLM systems
Senior AI Infrastructure Engineer specializing in LLM systems and real-time ML platforms
Sri Lekha Kandadai
Mid-level Machine Learning Engineer specializing in MLOps, monitoring, and multimodal AI
“ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.”
Narasimha Jwalapuram
Mid-level Applied AI Engineer specializing in LLM agents, RAG, and model alignment
“Applied Scientist with legal-tech experience who builds production LLM systems. Created and deployed Quibo AI, a LangGraph-based multi-agent pipeline that turns large markdown/Jupyter inputs into polished blogs and social posts, overcoming context limits via ChromaDB + HyDE RAG. Also built a large-scale iterative code-evolution workflow using multi-model orchestration (GPT/Claude/Gemini) with testing, debugging loops, and evaluation/observability practices.”
Abhinay Dodda
Mid-Level AI Engineer specializing in data pipelines and scalable ML systems
“Data engineer/backend developer with experience owning end-to-end, high-volume data pipelines for ML/analytics using Python, Airflow, SQL, and PySpark, reporting ~30% error reduction through improved reliability and data quality checks. Has also built Django-based REST APIs with caching/pagination and strong versioning practices, and operated external data collection/web scraping pipelines with anti-bot measures, monitoring, retries, and idempotent backfills.”
Jiaqi Li
Junior AI Engineer specializing in healthcare analytics and compliance
“Primary engineer at Customer Insights AI who built an end-to-end Python pipeline for 340B drug pricing compliance, using ML to detect suspicious pharmaceutical claims and benefit diversion. Stands out for combining healthcare compliance domain knowledge with production reliability practices, and for turning ambiguous analyst-driven review processes into automated workflows that cut manual review by 70%.”
Mid-level AI/ML Engineer specializing in LLMs, agentic systems, and MLOps
Principal Salesforce Architect specializing in AI, AWS, and enterprise integrations
Mid-level AI & Machine Learning Engineer specializing in computer vision and MLOps
Mid-level AI Engineer specializing in LLMs, RAG chatbots, and cloud AI testing
Mid-level AI/ML Engineer specializing in NLP, Computer Vision, and Generative AI
Senior AI/ML Engineer specializing in Generative AI and conversational systems