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Build and deploy production-grade AI/ML models using Python, TensorFlow, PyTorch, and MLOps tooling for scalable, real-world applications.
Build Python/C++ frameworks and APIs to run Vision and Generative AI models efficiently on custom AI accelerators, profiling and optimizing performance-critical code.
Design and build ML models, optimize them for performance, and collaborate with data scientists and engineers to solve business problems using Python or R.
Build and deploy generative AI chatbots, predictive models, and analytics dashboards using Python, LLMs, and tools like Streamlit and Power BI.
Builds and tests AI/ML models, preprocesses data, and helps deploy AI-driven features using Python and libraries like scikit-learn.
Build and deploy AI systems including LLMs, computer vision, and autonomous agents using Python, PyTorch, and LangChain, then productionize them with MLOps on cloud platforms.
Build and scale AI agents and ML pipelines using Python, PyTorch/TensorFlow, and frameworks like LangChain; integrate LLMs, vector DBs, and cloud-native systems.
Lead end-to-end training of large language models using domain-adaptive pretraining, fine-tuning, and reinforcement learning, while building robust data and evaluation pipelines for intelligent agent systems.
Build and maintain AI-driven test automation frameworks for mobile apps, using ML to predict defects and self-heal scripts while integrating with CI/CD pipelines.
Build and deploy LLM/NLP models using Hugging Face, LangChain, and cloud AI services; implement RAG pipelines and vector search for AI-driven chatbots and Q&A systems.
Design and deploy AI/ML models, including LLMs and GenAI, to solve healthcare data challenges using Python, cloud platforms, and MLOps.
Build and deploy production-grade ML models and RAG pipelines to replace rule-based systems, focusing on model quality, evaluation, and self-hosted LLM inference.
Build and optimize AI/ML models using Python, TensorFlow, and PyTorch, focusing on RAG, LangChain, and locally run AI with Ollama for production deployment.
Designs and integrates AI/ML solutions into a data lakehouse using Spark, Kafka, and Jupyter Enterprise Gateway for batch and real-time inference.
Build, optimize, and deploy AI/ML models (including LLMs) using Python, TensorFlow/PyTorch, and cloud platforms in a product-focused team.
Designs and architects AI/ML solutions integrating batch and real-time inference using Spark, Kafka, and Jupyter Enterprise Gateway within a data lakehouse.
Senior AI Engineer designs and deploys production-ready ML/LLM models, builds data pipelines, and mentors junior engineers for Devsinc’s client projects.
Build and fine-tune AI models using Python, TensorFlow/PyTorch, and cloud services like AWS Bedrock and Azure OpenAI to deliver industry-specific solutions.
Builds scalable data pipelines and AI/ML models in Python/SQL, deploys them via MLOps, and maintains cloud-based data infrastructure for intelligent applications.
Build and deploy ML pipelines and LLM services for a healthcare AI platform that supports population health and clinical decision-making.
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