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Build and deploy AI/ML models (LLMs, RAG, NLP, generative AI) on a large-scale platform using Python, TensorFlow, Scikit-learn, and AWS, with full MLOps and data engineering responsibilities.
Design and implement scalable AI architectures, collaborating with cross-functional teams to drive innovation and efficiency using technologies like AWS Bedrock, SageMaker, Azure OpenAI, Vertex AI, Python, PyTorch, and RAG.
Leads AI-driven product initiatives and the product lifecycle from conception to launch, collaborating with cross-functional teams using agile methodologies and technologies like Generative AI, LLMs, and cloud AI platforms.
AI QA Engineer validating AI-driven solutions through automated testing, API/ETL testing, model validation, and bias detection using Python, Selenium, and CI/CD tools on-site in Delhi.
Lead MLOps strategy and production ML system deployment for an AI company, defining multi-year technical roadmaps, architecting scalable systems, and establishing engineering standards—onsite in Delhi.
Remote Voice AI Engineer owning a real-time voice platform—tuning ASR/LLM/TTS/RAG pipelines, handling WebRTC/SIP telephony, and taking client implementations from scoping through production.
Build and own production AI systems (voice agents, RAG pipelines, LLM orchestration) while mentoring engineers and advising C-level clients in a hybrid San Diego role.
Founding engineer building an Enterprise Intelligence AI platform from 0-to-1, working across the full stack on AI agents, LLM APIs, RAG, and agentic workflows in an early-stage startup.
Onsite Applied AI Engineer in Los Angeles building end-to-end AI features (LLM, computer vision) from conception to deployment using Python, TensorFlow, Django, Flask, and AWS.
Builds and deploys AI-driven software systems (LLM/RAG, full-stack, cloud) for Adé AI’s enterprise clients, focusing on scalability, mentorship, and cross-functional collaboration in a hybrid work environment.
Senior AI Field Engineer helping AI-native customers adopt and scale Fireworks AI's generative AI platform through technical discovery, POCs, integration, and performance engineering.
Build and fine-tune AI models using Python, PyTorch, and Hugging Face; develop NLP and computer vision applications in a startup environment.
AI/ML Engineer building and deploying generative AI, NLP, and computer vision models using Python, PyTorch, Keras, and Hugging Face, with RESTful API development for model integration.
AI/ML Intern building and fine-tuning LLMs and other models using Python, PyTorch, and Hugging Face, with exposure to NLP, computer vision, and API development in Pune, India.
Develops and maintains Python-based applications with ML integration, APIs, and microservices for high-performance solutions; collaborates with data engineering teams to optimize and deploy scalable systems.
Design and deploy ML systems (CNN, RNN, LSTM), optimize MLOps pipelines, and run distributed training using Spark and Kubernetes in a fully remote, short-term contract role.
An Applied AI Scientist designing, developing, and deploying machine learning models (NLP, TensorFlow/PyTorch) in cloud environments using Python and Docker, in a hybrid role based in Acalanes Ridge, California.
Design and deploy advanced ML models (CNNs, RNNs, LSTMs) using Kubernetes and MLOps pipelines; optimize models for scale with distributed training and Apache Spark.
AI Consultant providing technical leadership for building and deploying scalable ML/LLM/SLM applications—including RAG pipelines, custom agents, multimodal systems, and cloud/MLOps infrastructure—on a part-time remote contract basis.
Builds and maintains scalable backend systems and APIs for AI-driven applications, focusing on FastAPI, microservices, real-time streaming (LiveKit), and AI/ML integrations while optimizing deployment pipelines.
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