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Vetted Model Deployment Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer & Data Scientist specializing in NLP and production ML
Mid-level AI/ML Engineer specializing in healthcare analytics and generative AI
Mid-level AI/ML Engineer specializing in GenAI, NLP, and AWS MLOps
Mid-Level Software Engineer specializing in cloud-native microservices and data platforms
Mid-level Machine Learning Engineer specializing in production MLOps and healthcare analytics
Senior Backend Software Engineer specializing in distributed systems and data pipelines
Mid-level GenAI/MLOps Engineer specializing in banking and healthcare LLM applications
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and cloud deployment
Mid-level Machine Learning Engineer specializing in MLOps, fraud detection, and data security
Mid-level AI Systems Engineer specializing in agentic evaluation and multimodal voice agents
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Senior Machine Learning Software Engineer specializing in Azure enterprise AI
Junior Machine Learning Software Engineer specializing in cloud-deployed predictive models
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Shreevershith Kollabettu
Mid-level AI Engineer specializing in LLMs, agentic systems, and MLOps
“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and Azure
“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”
Senior Backend Python Engineer specializing in cloud-native APIs and data platforms
Mid-level AI/ML Engineer specializing in financial risk, NLP, and MLOps
Sravani Gangaraju
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”
Mahesh Ponnam
Mid-level Data Scientist specializing in credit risk, fraud detection, and ESG analytics
“AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.”