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Build and maintain .NET-based cloud migration tools in Azure, designing secure APIs and integrating legacy systems into modern cloud architectures.
Build and deploy backend services for AI/ML models, integrating LLMs, vector databases, and RAG pipelines using Python, FastAPI, and cloud platforms.
Build and maintain LLM servers (Llama, Mistral, GPT API) and deploy AI agents that automate tasks like reminders, reports, and paperwork using vector databases and RAG pipelines.
Build scalable data pipelines and infrastructure for AI systems, including LLMs and agentic architectures, using Python, SQL, and cloud platforms like AWS/Azure/GCP.
Build and maintain scalable data pipelines and AI-ready data foundations for cutting-edge AI systems, including LLMs and agentic architectures, while collaborating with cross-functional teams.
Lead a team building generative AI and data-science solutions using Python, LLMOps, and cloud services; design multi-agent patterns, reasoning loops, and scalable microservices.
Design and build generative AI and data-science solutions using Python, TensorFlow/PyTorch, and LangChain, then deploy them as scalable microservices on AWS/GCP/Azure.
Lead a team to design and build generative AI and data-science solutions using Python, TensorFlow/PyTorch, LangChain, and cloud services, while overseeing MLOps, microservices, and CI/CD pipelines.
Lead AI Engineer designing and implementing generative AI and data-science solutions, including multi-agent systems, reasoning loops, and MLOps tooling in Python/Java with cloud providers.
Build and deploy GenAI solutions (RAG, agents, assistants) for enterprise clients, turning AI challenges into measurable results in weeks while balancing speed, cost, and governance.
Build and deploy AI-powered document processing systems using Python, NLP, OCR, and LLMOps in a production Kubernetes environment.
Build and scale data-driven and generative AI solutions for global clients in banking, pharma, and public sector using cloud platforms, Spark, and Azure OpenAI.
Build and run the AIOps platform that keeps AI models, LLM pipelines, and agents reliable, scalable, and cost-efficient in AWS/Azure.
Build and maintain cloud/hybrid CI/CD pipelines, Kubernetes clusters, and automation toolchains for Siemens Mobility’s transport-focused SaaS platforms.
DevOps Engineer to design, deploy, and maintain AI infrastructure using Kubernetes, Docker, and LLMOps tools like Ollama and LangChain, ensuring scalable, secure systems for logistics and industrial clients.
DevOps Engineer building and scaling AI infrastructure (LLMOps) with Kubernetes, Docker, and NVIDIA CUDA on Linux, automating deployments and integrating Python/Java services.
Build production-ready GenAI and agentic AI systems for global enterprises using Python, LLM APIs, and cloud platforms like AWS/Azure/GCP.
Build enterprise-grade GenAI applications and autonomous multi-agent systems using Python, LLM APIs, and vector databases, then deploy them to cloud platforms for Fortune 500 clients.
Build scalable data pipelines and infrastructure for AI systems using Python, SQL, Spark, and cloud platforms to power LLMs, retrieval systems, and agentic architectures.
Build and maintain high-throughput data pipelines in Databricks and AWS to ingest, normalize, and enrich financial and alternative data for AI/ML models.
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