Data Science Solutions Architect

6 days ago

rajkot, India beBeeAI Full time

Job DescriptionAbout our organization:We are a leader in simplifying enterprise private clouds. Our flagship product turns existing infrastructure into a full-featured private cloud.Enterprise IT teams can manage VMs and containers with familiar GUI tools and automated APIs in a secure environment.Key ResponsibilitiesDevelop intelligent AI solutions using state-of-the-art AI technologies to build pioneering NLP and Generative AI solutions that solve real-world infrastructure problems.Build and deploy agentic AI platforms and applications enabling autonomous execution and orchestration.Integrate AI/ML solutions into CI/CD pipelines, monitoring, and platform APIs.Collaborate with cross-functional engineers to deliver high-impact operational experience.Serve as a subject matter expert on a wide range of ML techniques and optimizations.Mentor & share best practices, guiding junior engineers and peers on ML design patterns, code quality, and experiment methodology.Qualifications and SkillsMinimum bachelor's degree in data science or a related field.5+ years of experience, with at least 2 years working on generative AI technologies.Solid understanding of transformers and modern NLP / LLM techniques; experience with fine-tuning or prompting large language models.Strong proficiency in Python; additional exposure to Golang is a plus.Working knowledge of deployment using Kubernetes on cloud infrastructure is desirable.Proven ability to build scalable, reliable production services.Ability to work on tasks and projects through to completion with limited supervision.Distinguish YourselfAgentic AI mastery, with practical experience using frameworks such as LangChain or LangGraph and a deep understanding of multi-step reasoning and planning.LLM inference optimization, with expertise in accelerating LLM inference (e.g., KV caching, quantization) to achieve low latency at scale.End-to-end ML systems ownership, with a portfolio showing full lifecycle ownership from data ingestion to monitoring and continuous improvement.

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