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Vetted Model Deployment Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in generative AI, NLP, and MLOps
Mid-level Data Scientist specializing in NLP, Generative AI, and ML pipelines
Senior AI/ML Engineer specializing in NLP, computer vision, and cloud ML systems
Mid-level MLOps/Machine Learning Engineer specializing in cloud-native production ML
Junior Machine Learning Engineer specializing in Generative AI and MLOps
Junior Computer Vision Engineer specializing in AI/ML and transformer-based vision models
Junior Machine Learning Engineer specializing in scalable ML systems and LLMs
Mid-Level Software Developer specializing in AI/ML and cloud-native microservices
Mid-level Generative AI Engineer specializing in LLMs, RAG, and NLP systems
Senior Software Engineer specializing in backend ML and Generative AI platforms
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”
Balakrishna Mylapilli
Mid-level AIML Engineer specializing in production ML and MLOps
“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”
Prasad Sadineni
Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems
“Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).”