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Vetted Azure Machine Learning Professionals
Pre-screened and vetted.
Meghana Chowdary Borra
Junior Machine Learning Engineer specializing in predictive modeling and GenAI RAG systems
“LLM engineer who built and deployed an emotionally intelligent AAC communication system using an emotion-aware RAG pipeline (Empathetic Dialogues + GoEmotions) and a PEFT-adapted model. Experienced with LangChain/LangGraph and custom Python orchestration, focusing on reliability (guards, schema validation, fallbacks), latency optimization, and rigorous evaluation (automatic metrics + human-in-the-loop), with a reported 18% user satisfaction improvement.”
Lakshmi Meghana
Mid-level AI/ML Engineer specializing in production ML, MLOps, and NLP
“Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.”
Krishna K
Junior Machine Learning Engineer specializing in multimodal systems and LLMs
“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”
Priyansh Aggarwal
Junior Software Engineer specializing in AI/ML and full-stack web development
“Built core perception and decision layers for a 3D AI-powered interactive avatar/agent with a robotics-like perception–reasoning–action loop, combining computer vision, NLP, and real-time response. Focused on making multimodal inputs robust (normalization, intent + emotion signal fusion) and improving real-time performance via instrumentation, profiling, and parallelization; also designed distributed, loosely coupled state-based communication and deployed services with Docker.”
Director-level IT & Operations leader specializing in cloud infrastructure and cybersecurity
Mid-level AI/ML Engineer specializing in LLM systems, MLOps, and real-time fraud detection
Mid-level AI/ML Engineer specializing in MLOps and healthcare machine learning
Senior Machine Learning Engineer specializing in LLMs, RAG, and agentic AI systems
Senior Data Scientist and AI/ML Engineer specializing in LLMs, NLP, and RAG systems
Senior Machine Learning Engineer specializing in NLP, computer vision, and LLMs
Mid-level MLOps Engineer specializing in production machine learning systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and cloud AI infrastructure
Principal AI/ML Engineer specializing in LLMs, RAG pipelines, and production MLOps
Senior AI/ML Engineer specializing in LLM agents and scalable ML platforms
Senior Data Engineer specializing in MLOps, LLMs, and computer vision
Mid-level AI/ML Engineer specializing in LLM, RAG, and semantic search systems