2 Gpu Infrastructure Jobs

Key Roles and Responsibilities: Solution Architecture & Technical Leadership Demonstrate deep expertise in LLMs such as Phi-4, Mistral, Gemma, Llama and other foundation models Assess client business requirements and translate them into detailed technical specifications Recommend appropriate LLM solutions based on specific business outcomes and use cases Experience in sizing and architecting infrastructure for AI/ML workloads, particularly GPU-based systems. Design scalable and secure On-Prem/Private AI architectures Create technical POCs and prototypes to demonstrate solution capabilities Hands-on experience with vector databases (open-source or proprietary), such as Weaviate, Milvus, or Vald etc. Expertise in fine-tuning, query caching, and optimizing vector embeddings for efficient similarity searches Business Development Size and qualify opportunities in the On-Prem/Private AI space Develop compelling proposals and solution presentations for clients Build and nurture client relationships at technical and executive levels Collaborate with sales teams to create competitive go-to-market strategies Identify new business opportunities through technical consultation Project & Delivery Leadership Work with delivery teams to develop end-to-end solution approaches and accurate costing Lead technical discovery sessions with clients Guide implementation teams during solution delivery Ensure technical solutions meet client requirements and business outcomes Develop reusable solution components and frameworks to accelerate delivery AI Agent Development Design, develop, and deploy AI-powered applications leveraging agentic AI frameworks such as LangChain, AutoGen, and CrewAI. Utilize the modular components of these frameworks (LLMs, Prompt Templates, Agents, Memory, Retrieval, Tools) to build sophisticated language model systems and multi-agent workflows. Implement Retrieval Augmented Generation (RAG) pipelines and other advanced techniques using these frameworks to enhance LLM responses with external data. Contribute to the development of reusable components and best practices for agentic AI implementations. Knowledge, Skills, and Attributes: Basic Qualifications: 8+ years of experience in solution architecture or technical consulting roles 3+ years of specialized experience working with LLMs and Private AI solutions Demonstrated expertise with models such as Phi-4, Mistral, Gemma, and other foundation models Strong understanding of GPU infrastructure sizing and optimization for AI workloads Proven experience converting business requirements into technical specifications Experience working with delivery teams to create end-to-end solutions with accurate costing Strong understanding of agentic AI systems and orchestration frameworks Bachelors degree in computer science, AI, or related field Ability to travel up to 25% Preferred Qualifications: Master's degree or PhD in Computer Science or related technical field. Experience with Private AI deployment and fine-tuning LLMs for specific use cases Knowledge of RAG (Retrieval Augmented Generation) and enterprise knowledge systems Hands-on experience with prompt engineering and LLM optimization techniques Understanding of AI governance, security, and compliance requirements Experience with major AI providers: OpenAI/Azure OpenAI, AWS, Google, Anthropic, etc. Prior experience in business development or pre-sales for AI solutions Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders Strong problem-solving abilities and analytical mindset

Deployment and Operations: Responsible for the continuous deployment, integration, and monitoring of ML models. Use AWS DevOps and MLOps tools to manage workflows and maintain uptime. Automation and CI/CD: Implement CI/CD pipelines for model deployment and integration with data pipelines. Automate testing, validation, and retraining of models to support scalability. Infrastructure Management: Manage underlying AWS infrastructure (EC2, S3, Lambda, etc.) required for model hosting. Optimize resource allocation to minimize costs while meeting performance needs. Data Pipeline Maintenance: Support and maintain data ingestion, transformation, and feature engineering pipelines. Ensure seamless integration with production workflows and data updates. Monitoring and Logging: Implement monitoring solutions to track model performance and health. Maintain logs, set up alerts, and work on troubleshooting any issues. Security and Compliance (Operational Focus): Implement and enforce security policies for data and model access. Ensure compliance from an operational standpoint (e.g., permissions, audit logs). Collaboration and Support: Work closely with data scientists to facilitate model handovers. Provide support in resolving issues with model deployment and performance. Continuous Improvement: Identify opportunities for process improvements and automation. Experiment with new MLOps tools and methods to enhance pipeline efficiency.