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| AI Engineer with 4+ years designing and deploying production-grade Generative AI, LLMs, and agentic AI systems across pharmaceutical and retail domains. Expertise in RAG pipelines, LangChain/LangGraph, fast API microservices on AWS/Azure, and end-to-end ML lifecycle. |
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Career Objectives:
Aspire to design and deploy scalable AI/ML systems, with a focus on Generative AI, LLMs, and multi-agent orchestration to accelerate enterprise drug development, regulatory processes, and clinical decision support.
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| Generative AI Engineer with 3+ years of experience building and deploying production-grade LLM systems, RAG pipelines, and agentic AI frameworks across AWS, GCP, and Azure. Delivered AI solutions reducing manual effort by 60%+ in regulated enterprise environments. Pursued MS in Computer Science at University at Buffalo. |
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Career Objectives:
To leverage Generative AI expertise in building production-grade LLM systems and scalable AI solutions across cloud platforms.
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| AI/ML Engineer with 3+ years of experience designing, deploying, and scaling production Machine Learning and Generative AI solutions for enterprise applications. Experienced in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, Agentic AI workflows, AI microservices, and end-to-end MLOps pipelines. |
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Career Objectives:
AI/ML Engineer focused on designing, deploying, and scaling production ML and Generative AI solutions for enterprise applications.
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| AI/ML Engineer with 4+ years of experience building and deploying production ML systems across financial services and enterprise software. Skilled in end-to-end pipelines, Generative AI, RAG systems, and multi-agent workflows using Python, PyTorch, LangChain, and Azure OpenAI. Experienced in scaling ML infrastructure on Spark and Databricks with strong MLOps and model governance practices. |
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| Agentic AI Engineer with 4+ years of experience architecting and deploying autonomous LLM systems, Enterprise RAG pipelines, and multi-agent orchestration frameworks across financial services and telecom environments. |
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Career Objectives:
Agentic AI Engineer focusing on architecting and deploying autonomous LLM systems, Enterprise RAG pipelines, and multi-agent orchestration.
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| Over 19 years of enterprise infrastructure experience specializing in multi-agent LLM orchestration, RAG pipeline design, MCP integration, and AWS Bedrock. |
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Career Objectives:
Forward-deployed engineer and AI systems architect delivering enterprise-scale AI, multi-agent systems, and scalable infrastructure.
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| Software Engineer with 2 years of experience deploying production AI systems, client-facing agentic workflows, and LLM integrations; strong background in full-stack development and DevOps. |
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Career Objectives:
Pursuing roles in AI/ML engineering focusing on production AI systems and LangGraph integration.
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| Forward Deployed Engineer with 4+ years of experience integrating AI models, data pipelines, and intelligent workflows into enterprise environments. Proficient in Python, SQL, and cloud infrastructure with hands-on ML Ops. |
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Career Objectives:
Seeking opportunities in AI/ML engineering roles focusing on deploying production-grade AI systems.
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| 5+ years building production-grade AI and data systems across healthcare, insurance, and financial services; experience with LangChain, OpenAI API, RAG pipelines, Palantir Foundry; full-stack Python/TypeScript development; cloud infrastructure on AWS/Azure/GCP. |
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Career Objectives:
Forward Deployed Engineer focused on AI/data systems with enterprise impact, leveraging LLM-powered workflows and scalable data pipelines.
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| Seasoned CLM Product Owner with 17 years in CLM and 13 years in product ownership. Delivered 30+ CLM solutions, led vendor selection (iCertis, Conga, Malbek, IronClad), and guided end-to-end CLM implementations for major clients such as SanDisk, Nasdaq, VMware, McKesson, Nuance, Twitter, Open Society Foundation, Monster Energy, Nokia, and others. |
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Career Objectives:
Looking to build the next generation of AI Native Contract Lifecycle Management (CLM) Solutions for any market.
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