2 Gpu Infrastructure Jobs

Job Summary: We are looking for a highly capable and automation-driven MLOps Engineer with 2+ years of experience in building and managing end-to-end ML infrastructure. This role focuses on operationalizing ML pipelines using tools like DVC, MLflow, Kubeflow, and Airflow, while ensuring efficient deployment, versioning, and monitoring of machine learning and Generative AI models across GPU-based cloud infrastructure (AWS/GCP). The ideal candidate will also have experience in multi-modal orchestration, model drift detection, and CI/CD for ML systems. Key Responsibilities: Develop, automate, and maintain scalable ML pipelines using tools such as Kubeflow, MLflow, Airflow, and DVC. Set up and manage CI/CD pipelines tailored to ML workflows, ensuring reliable model training, testing, and deployment. Containerize ML services using Docker and orchestrate them using Kubernetes in both development and production environments. Manage GPU infrastructure and cloud-based deployments (AWS, GCP) for high-performance training and inference. Integrate Hugging Face models and multi-modal AI systems into robust deployment frameworks. Monitor deployed models for drift, performance degradation, and inference bottlenecks, enabling continuous feedback and retraining. Ensure proper model versioning, lineage, and reproducibility for audit and compliance. Collaborate with data scientists, ML engineers, and DevOps teams to build reliable and efficient MLOps systems. Support Generative AI model deployment with scalable architecture and automation-first practices. Qualifications: 2+ years of experience in MLOps, DevOps for ML, or Machine Learning Engineering. Hands-on experience with MLflow, DVC, Kubeflow, Airflow, and CI/CD tools for ML. Proficiency in containerization and orchestration using Docker and Kubernetes. Experience with GPU infrastructure, including setup, scaling, and cost optimization on AWS or GCP. Familiarity with model monitoring, drift detection, and production-grade deployment pipelines. Good understanding of model lifecycle management, reproducibility, and compliance. Preferred Qualifications : Experience deploying Generative AI or multi-modal models in production. Knowledge of Hugging Face Transformers, model quantization, and resource-efficient inference. Familiarity with MLOps frameworks and observability stacks. Experience with security, governance, and compliance in ML environments. Location-Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

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