2 Mlops Pipelines Jobs

Job Title: Senior Generative AI Developer (Lead - Data Science) Gurgaon / Noida | 7-10 Years Experience About the Role We are looking for an experienced Senior Generative AI Developer to lead the design, development, and deployment of large-scale enterprise-grade GenAI systems. This is a hybrid role requiring both deep hands-on expertise and strategic leadership , working across multi-cloud environments and managing the full lifecycle of AI/ML systems. Key Responsibilities Technical Leadership Architect multi-LLM systems (e.g., Mixture-of-Experts, LLM routing) for performance-cost optimization. Design and scale training pipelines using FSDP, DeepSpeed on GPU/TPU clusters. Cloud-Native AI Development Build and manage multi-cloud GenAI platforms (Azure OpenAI, GCP Vertex AI, AWS Bedrock) with unified MLOps. Implement enterprise-grade security: VPC peering, private endpoints , and regulatory compliance (e.g., data residency). Innovation & Strategy Drive pioneering initiatives such as Agentic workflows , real-time fine-tuning, and synthetic data generation . Define and execute AI governance : model cards, drift monitoring, red-teaming protocols. Cross-Functional Impact Collaborate with product and business teams to define GenAI strategy and ROI metrics (e.g., automation cost savings). Mentor junior engineers and promote GenAI best practices across the organization. Required Qualifications Education : Bachelors/Master’s in CS, AI/ML, or equivalent experience. Experience : 5+ years in ML, 2+ years specifically in Generative AI. Technical Mastery Languages : Expert in Python Frameworks : PyTorch, TensorFlow Extended (TFX), ONNX Runtime Certifications : Azure AI Engineer Expert, GCP ML Engineer (preferred) GenAI Expertise Experience shipping production-scale GenAI systems (e.g., 10k+ QPS chatbots, GitHub Copilot-scale models). Mastery of advanced LLM techniques (LLM orchestration, guardrails, self-reflective prompting). Must-Have Experience Azure : Azure OpenAI, MLOps Pipelines, Cognitive Search GCP : Vertex AI Evaluation, Gemini Multimodal, TPU v5 Pods Built RAG systems using hybrid search (vector + keyword) and real-time data hydration Led AI compliance in regulated domains (finance, healthcare) Preferred Additions Experience with multi-cloud GenAI deployments (e.g., training on GCP, serving on Azure). Certification in Azure and/or GCP AI tracks. Exposure to autonomous agents, vector databases , and AI-driven business automation.

The Opportunity Are you passionate about building intelligent, enterprise-grade AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic frameworks? At Nutanix, we're looking for a skilled and experienced AI/ML engineer to help shape the future of generative AI within our SaaS Engineering organization. As a senior member of our team, youll work at the cutting edge of AI innovationdeveloping and deploying state-of-the-art LLMs and embedding models, optimizing model performance, and building scalable ML pipelines with real-world impact. About the Team At Nutanix, you will be joining a dynamic central platform team that plays a pivotal role in revolutionizing our approach to artificial intelligence and machine learning within the SaaS Engineering group. Comprising eight experienced engineers, our team specializes in addressing the GenAI, machine learning, and data science needs of various squads within the organization. Our diverse skill set ensures we collaborate effectively to create innovative solutions, leveraging the latest advancements in technology to drive our initiatives forward. Your Role Design and deploy Retrieval-Augmented Generation (RAG) pipelines . Build, fine-tune, and deploy LLMs and embedding models such as LLaMA 3 , Gemma , Mistral , and other domain-specific transformers. Fine-tune both LLMs and embedding models for specialized enterprise tasks including Q&A, summarization, classification, and conversational AI. Develop and maintain agentic frameworks capable of orchestrating task-specific intelligent agents with memory, planning, and tool-use capabilities. Build and evaluate custom agents for use cases like document analysis, data querying, and interactive user support. Implement evaluation frameworks for LLM outputs, including both automated metrics and task-specific success criteria. Work closely with data engineering teams to develop custom training pipelines and extract meaningful insights from large-scale internal datasets. Develop MLOps pipelines for training, deployment, and monitoring using tools like MLflow , Kubeflow , and custom CI/CD workflows. Deploy optimized inference endpoints for high-performance, low-latency model serving at scale. Manage vectorization workflows using advanced embedding models and vector databases for semantic search and content retrieval. Demonstrate working knowledge of LangChain, OpenAI function-calling, vector databases and scalable retrieval logic. Work with Kubernetes clusters to provision, scale, and monitor AI/ML workloads; understand GPU, CPU, and storage hardware requirements for efficient deployment. Collaborate with cross-functional teams including backend, data, and infrastructure engineers to integrate models seamlessly into production systems. What You Will Bring Bachelors, Masters, or Ph.D. in Computer Science, Machine Learning, Applied Math, or a related field. 5+ years of hands-on experience building, deploying, and maintaining AI/ML systems in production environments. Strong foundation in MLOps, including model versioning, CI/CD, monitoring, and retraining workflows. In-depth understanding of Kubernetes (K8s) and GPU-based infrastructure, including container orchestration and GPU scheduling for AI workloads. Experience working with Elasticsearch for semantic search and integrating it within RAG or LLM-driven architectures. Proficient in Python (core ML libraries like PyTorch, Pandas, and NumPy). Hands-on experience using Jupyter Notebooks for experimentation, documentation, and collaboration. Comfortable with Unix-based systems, shell scripting, and command-line tooling for ML operations and debugging. Familiarity with LangChain, LLM orchestration, and vector database integration. Strong collaboration and communication skills, with the ability to mentor junior team members and drive initiatives independently. Open-source contributions or published work in the ML/AI domain is a plus.