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12.0 - 18.0 years
35 - 40 Lacs
Chennai
Work from Office
Tech stack required: Programming languages: Python Public Cloud: AzureFrameworks: Vector Databases such as Milvus, Qdrant/ ChromaDB, or usage of CosmosDB or MongoDB as Vector stores. Knowledge of AI Orchestration, AI evaluation and Observability Tools. Knowledge of Guardrails strategy for LLM. Knowledge on Arize or any other ML/LLM observability tool. Experience: Experience in building functional platforms using ML, CV, LLM platforms. Experience in evaluating and monitoring AI platforms in production Nice to have requirements to the candidate Excellent communication skills, both written and verbal. Strong problem-solving and critical-thinking abilities. Effective leadership and mentoring skills. Ability to collaborate with cross-functional teams and stakeholders. Strong attention to detail and a commitment to delivering high-quality solutions. Adaptability and willingness to learn new technologies. Time management and organizational skills to handle multiple projects and priorities.
Posted 2 weeks ago
10.0 - 19.0 years
22 - 37 Lacs
Chennai, Bengaluru
Hybrid
GenAI Architect - Fulltime Remote Shift:General Shift Job Summary: We are looking for a GenAI Architect with deep expertise in AI/Gen AI systems, specifically in designing and building large-scale AI solutions. As a GenAI Architect , you will be responsible for leading the design and architecture of cutting-edge Gen AI systems, with a focus on large language models (LLMs), AI frameworks, and advanced machine learning technologies. This is a senior role requiring strategic vision and hands-on expertise in AI solution architecture, software engineering, and MLOps practices. Responsibilities: Architecting AI Solutions : Design and architect scalable, efficient, and robust GenAI systems leveraging advanced machine learning frameworks, particularly in large language models (LLMs). Leading AI Framework Implementation : Lead the design and implementation of complex GenAI models and frameworks using Python and other relevant technologies. Provide strategic oversight of AI/GenAI architecture to align with business needs and tech stack. Model Optimization and Performance : Oversee the optimization of large-scale AI models for both training and inference, ensuring high performance and scalability across platforms. API Architecture & Development : Guide the development of high-performance APIs for AI services using FastAPI or other relevant frameworks, ensuring ease of integration with internal and external systems. MLOps Integration : Drive the integration of MLOps pipelines for seamless deployment, monitoring, and scaling of machine learning models, ensuring automation, efficiency, and high availability. Leadership and Mentorship : Provide technical leadership and mentorship to teams of engineers, guiding them in best practices for AI system architecture, software engineering, and AI model lifecycle management. Collaboration with Cross-Functional Teams : Work closely with Data Science, Data Engineering, DevOps, and Product teams to ensure AI/Gen AI solutions are architected in a way that meets both technical and business requirements. CI/CD & Automation : Design and implement automated pipelines for continuous integration and continuous deployment (CI/CD) of AI models, ensuring smooth and rapid updates to production environments. DevOps Practices : Ensure the robust deployment and management of AI models in production environments using DevOps tools like Docker, Kubernetes, Jenkins, and Terraform. Security and Compliance : Ensure AI systems adhere to best practices in security, privacy, and compliance, especially in handling sensitive data and large-scale model training. Key Requirements: Experience : Hands-on experience in AI/Gen AI system design, implementation, and scaling, with expertise in large language models (LLMs) and AI frameworks. Programming Skills : Expertise in Python and a strong understanding of data manipulation libraries like Pandas and NumPy. AI Frameworks : Experience in designing and managing large-scale AI models using cutting-edge frameworks such as TensorFlow, PyTorch, and similar. API Development : Strong experience in FastAPI or other modern frameworks for building high-performance APIs. Software Engineering : Proficient in software engineering principles, version control (Git), and maintaining code quality through automated testing and CI/CD pipelines. MLOps & ML Engineering : Deep understanding of MLOps practices for model deployment, monitoring, and optimization. Hands-on experience with ML engineering workflows. DevOps Expertise : Strong experience with DevOps tools like Docker, Kubernetes, Jenkins, and Terraform, with an emphasis on automating deployment and managing infrastructure at scale. Leadership & Mentorship : Proven experience in leading technical teams, making architectural decisions, and mentoring junior engineers.
Posted 3 weeks ago
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