4 Model Registry Jobs

As an MLOps Engineer at our company, you will be responsible for developing scalable applications and platforms in an agile environment. You will manage various aspects of project management, including requirements gathering, metadata collection, classification, security clearance, data pipeline template development, and data pipeline monitoring and support. Your role will involve designing, developing, deploying, and maintaining production-grade scalable data transformation, machine learning, and deep learning code and pipelines. You will be in charge of managing the ETL and machine learning model lifecycle, including development, deployment, monitoring, maintenance, and updates of data and models in production. Additionally, you will build and maintain tools and infrastructure for data processing for AI/ML development initiatives. To succeed in this role, you must have experience deploying machine learning models in production environments and possess a strong background in DevOps, Data Engineering, and ML on Cloud platforms. You should be familiar with containerization and orchestration tools like Docker and Kubernetes, as well as ML Ops open source frameworks for model registry, performance measurement, and monitoring. Proficiency in Python scripting, CI/CD, and Python data tools such as Pandas, Dask, or Pyspark is required. Experience working on large-scale distributed systems, Python/Scala for data pipelines, and Scala/Java/Python for micro-services and APIs will be beneficial. Knowledge of HDP, Oracle skills & SQL, Spark, Scala, Hive, and Oozie DataOps is also desirable. If you are a self-motivated individual with a passion for problem-solving and continuous learning, we encourage you to apply for this exciting opportunity and contribute to our AI/ML development initiatives. Regards, Manvendra Singh manvendra.singh@incedoinc.com,

Enterpret is a cutting-edge company specializing in AI-native applications that harness the potential of customer feedback to benefit businesses. By consolidating feedback from various sources, Enterpret transforms it into actionable insights that drive customer-centric decisions for teams at renowned companies such as Perplexity, Notion, Canva, and Figma. With the support of notable investors like Kleiner Perkins and Peak XV, Enterpret is revolutionizing how businesses comprehend and respond to the voice of their customers. As the LLMOps Architect at Enterpret, you will play a pivotal role in fine-tuning LLM models, managing prompts, conducting evaluations, optimizing for cost efficiency, and speed optimization both during the experimentation phase and in the production environment. This is a foundational role that entails high ownership, where you will collaborate closely with the OpenAI, Anthropic, and AWS teams to construct top-tier ML infrastructure. Working in tandem with ML researchers, backend engineers, and product teams, you will ensure the resilience, security, and cost-effectiveness of Enterpret's AI systems as the company experiences exponential growth. Key success factors include enhancing the speed of experimentation, reducing time to productionization, and elevating the quality of models. Reporting directly to the CTO, you will be responsible for several critical tasks. You will design and enhance Enterpret's ML platform utilizing AWS, Terraform, OpenAI, and Anthropic for training, serving, and retraining encoders and LLM models. Additionally, you will develop CI/CD pipelines tailored for ML, deploy and manage model serving systems for real-time inference and batch pipelines, establish observability for model performance and data drift, lead incident response and postmortems for ML systems, optimize cloud usage for ML workflows, implement governance and security measures, work on productionizing AI models, evaluate tools for model registry, feature stores, and orchestration, champion MLOps best practices, and mentor engineers. The ideal candidate for this role should possess a minimum of 6 years" experience in MLOps and ML infrastructure, with expertise in AWS, infrastructure-as-code, container orchestration, strong Python skills, hands-on experience with CI/CD systems, proficiency in monitoring and maintaining production ML systems, cloud cost optimization knowledge, familiarity with model serving stacks and experimentation tools, and a track record of mentoring and taking ownership of systems in production. Additionally, a passion for automation, proficiency with AI coding agents, and exposure to GenAI workflows and responsible AI practices are desirable. Enterpret offers a compelling opportunity to work at the heart of ML, take early ownership of impactful projects, collaborate with a talented team, operate in a focused and fast-paced environment, and enjoy competitive compensation, meaningful equity, comprehensive healthcare benefits, generous leave policies, and a team-centric culture built on trust and ownership. At Enterpret, we prioritize a culture of ownership, teamwork, personal care, constructive feedback, humility, continuous learning, and improvement. We are committed to providing equal opportunities for all individuals.,

As the AI Ops, ML Ops, and LLM Ops Manager, your primary responsibility is to oversee the efficient and scalable operations of AI and machine learning models. You will be in charge of managing the entire model lifecycle, covering development, deployment, monitoring, and maintenance. It is crucial to ensure strict adherence to predefined Service Level Agreements (SLAs) for AI and ML operations. To streamline the integration and deployment of models, you will be required to develop and maintain CI/CD Ops pipelines. Additionally, implementing and managing model registries for version control and governance is essential. You will establish coding checklists and best practices while also developing and automating testing frameworks to maintain model quality and reliability. Designing and managing inference pipelines for both real-time and batch predictions will be under your purview. Innovation plays a key role in this role, as you will be expected to adopt emerging technologies such as GenAI, AI, and NLP. Accelerating product/service development through rapid prototyping and iterative methods will be necessary to drive innovation effectively. Furthermore, aligning analytics innovation efforts with business strategy, IT strategy, and legal/regulatory requirements is imperative. You will also be tasked with identifying and developing advanced analytics capabilities and ecosystem partnerships in alignment with the DnA strategy. Key Responsibilities: - Lead AI Ops, ML Ops, and LLM Ops to ensure efficient and scalable operation of AI and machine learning models. - Develop and manage the model lifecycle, including development, deployment, monitoring, and maintenance. - Ensure adherence to predefined SLAs for AI and ML operations. - Create and manage analytics product/services roadmaps from concept to launch. - Develop and maintain CI/CD Ops pipelines for seamless integration and deployment of models. - Implement and manage model registries for version control and governance. - Establish and enforce coding checklists and best practices. - Develop and automate testing frameworks to ensure model quality and reliability. - Design and manage inference pipelines for real-time and batch predictions. - Incubate and adopt emerging technologies (GenAI, AI, NLP) to accelerate product/service development through rapid prototyping and iterative methods. - Align analytics innovation efforts with business strategy, IT strategy, and legal/regulatory requirements. - Establish and update strategies, implementation plans, and value cases for emerging technologies. - Drive innovation using appropriate people, processes, partners, and tools. - Identify and develop advanced analytics capabilities and ecosystem partnerships in alignment with DnA strategy. - Oversee end-to-end delivery of analytics services and products across cross-functional business areas. - Serve as the point of escalation, review, and approval for key issues and decisions. - Manage resource and capacity planning in line with business priorities and strategies. - Foster continuous improvement within the team. - Decide on program timelines, governance, and deployment strategies. Key Performance Indicators: - Achieved targets in Enterprise business case contribution, KPIs, customer satisfaction, and innovation measures. - Delivery on agreed KPIs including business impact - Launch of innovative technology solutions across Novartis at scale. - Business impact and value generated from DDIT solutions. - Adoption and development of Agile Productization and DevOps practices. - Operations stability and effective risk management. - Feedback on customer experience. - Applications adherence to ISC requirements and are audit ready. - Business capability, vision & strategy clearly defined, communicated, and executed, well aligned to business strategy and Enterprise IT strategy, providing a competitive advantage to Novartis. - Role model with the highest standards of professional conduct in leading the business capability area in line with the new IT operating model. - Deployment of digital platforms and services at scale to deliver the digital strategy. Skills And Experience: - Demonstrated experience in Budget Management, Business Acumen, Performance Management, Planning, Project Management, Risk Management, Service Delivery Management, and stakeholder management. - Strong understanding of AI Ops, ML Ops, and LLM Ops. - Experience in developing and managing the model lifecycle, including deployment and maintenance. - Proficiency in managing operations with predefined SLAs. - Expertise in CI/CD Ops pipelines development. - Experience with model registry and management. - Knowledge of coding checklists and best practices. - Proficiency in developing and automating testing frameworks. - Experience in designing and managing inference pipelines. - Production experience with commercial and open-source ML platforms. - Strong knowledge of AWS, Databricks, and Snowflake service offerings. - Ability to collaborate with business teams to gather requirements, groom product backlogs, and drive delivery. - Agile delivery experience managing multiple concurrent delivery cycles. - Solid foundation in CRISP analytical life cycle management. - Strong leadership skills with the ability to build high-performing teams. - Excellent vendor management and IT governance skills. - Innovative and analytical mindset with a focus on continuous improvement. - Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, Building High Performing Teams, Vendor Management, Innovative & Analytical Technologies. - Strong understanding of descriptive vs. prescriptive Analytical frameworks. - Strong knowledge of visualization platforms and project life cycle management, including Power BI, Qlik, and MicroStrategy. - Significant production experience addressing visualization platform and data pipeline performance constraints. - Strong analytical and problem-solving skills, effective communication, and the ability to influence and collaborate with cross-functional teams.,

At PwC, our data and analytics team focuses on utilizing data to drive insights and support informed business decisions. We leverage advanced analytics techniques to assist clients in optimizing their operations and achieving strategic goals. As a data analysis professional at PwC, your role will involve utilizing advanced analytical methods to extract insights from large datasets, enabling data-driven decision-making. Your expertise in data manipulation, visualization, and statistical modeling will be pivotal in helping clients solve complex business challenges. PwC US - Acceleration Center is currently seeking a highly skilled MLOps/LLMOps Engineer to play a critical role in deploying, scaling, and maintaining Generative AI models. This position requires close collaboration with data scientists, ML/GenAI engineers, and DevOps teams to ensure the seamless integration and operation of GenAI models within production environments at PwC and for our clients. The ideal candidate will possess a strong background in MLOps practices and a keen interest in Generative AI technologies. With a preference for candidates with 4+ years of hands-on experience, core qualifications for this role include: - 3+ years of experience developing and deploying AI models in production environments, alongside 1 year of working on proofs of concept and prototypes. - Proficiency in software development, including building and maintaining scalable, distributed systems. - Strong programming skills in languages such as Python and familiarity with ML frameworks like TensorFlow and PyTorch. - Knowledge of containerization and orchestration tools like Docker and Kubernetes. - Understanding of cloud platforms such as AWS, GCP, and Azure, including their ML/AI service offerings. - Experience with continuous integration and delivery tools like Jenkins, GitLab CI/CD, or CircleCI. - Familiarity with infrastructure as code tools like Terraform or CloudFormation. Key Responsibilities: - Develop and implement MLOps strategies tailored for Generative AI models to ensure robustness, scalability, and reliability. - Design and manage CI/CD pipelines specialized for ML workflows, including deploying generative models like GANs, VAEs, and Transformers. - Monitor and optimize AI model performance in production, utilizing tools for continuous validation, retraining, and A/B testing. - Collaborate with data scientists and ML researchers to translate model requirements into scalable operational frameworks. - Implement best practices for version control, containerization, and orchestration using industry-standard tools. - Ensure compliance with data privacy regulations and company policies during model deployment. - Troubleshoot and resolve issues related to ML model serving, data anomalies, and infrastructure performance. - Stay updated with the latest MLOps and Generative AI developments to enhance AI capabilities. Project Delivery: - Design and implement scalable deployment pipelines for ML/GenAI models to transition them from development to production environments. - Oversee the setup of cloud infrastructure and automated data ingestion pipelines to meet GenAI workload requirements. - Create detailed documentation for deployment pipelines, monitoring setups, and operational procedures. Client Engagement: - Collaborate with clients to understand their business needs and design ML/LLMOps solutions. - Present technical approaches and results to technical and non-technical stakeholders. - Conduct training sessions and workshops for client teams. - Create comprehensive documentation and user guides for clients. Innovation And Knowledge Sharing: - Stay updated with the latest trends in MLOps/LLMOps and Generative AI. - Develop internal tools and frameworks to accelerate model development and deployment. - Mentor junior team members and contribute to technical publications. Professional And Educational Background: - Any graduate / BE / B.Tech / MCA / M.Sc / M.E / M.Tech / Masters Degree / MBA,