6 Model Versioning Jobs

At EY, you'll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture, and technology to become the best version of you. And we're counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. We are looking to hire individuals with strong AI Enabled Automation skills who are interested in applying AI in the process automation space using technologies such as Azure, AI, ML, Deep Learning, NLP, GenAI, large Lang Models (LLM), RAG, Vector DB, Graph DB, and Python. Responsibilities: - Development and implementation of AI enabled automation solutions, ensuring alignment with business objectives. - Design and deploy Proof of Concepts (POCs) and Points of View (POVs) across various industry verticals to demonstrate the potential of AI enabled automation applications. - Ensure seamless integration of optimized solutions into the overall product or system. - Collaborate with cross-functional teams to understand requirements, integrate solutions into cloud environments (Azure, GCP, AWS, etc.), and ensure alignment with business goals and user needs. - Educate the team on best practices and keep updated on the latest tech advancements to bring innovative solutions to the project. Technical Skills Requirements: - 9 to 13 years of relevant professional experience. - Proficiency in Python and frameworks like PyTorch, TensorFlow, Hugging Face Transformers. - Strong foundation in ML algorithms, feature engineering, and model evaluation (Must). - Strong foundation in Deep Learning, Neural Networks, RNNs, CNNs, LSTMs, Transformers (BERT, GPT), and NLP (Must). - Experience in GenAI technologies such as LLMs (GPT, Claude, LLaMA), prompting, fine-tuning. - Experience with LangChain, LlamaIndex, LangGraph, AutoGen, or CrewAI (Agentic Framework). - Knowledge of retrieval augmented generation (RAG) and Knowledge Graph RAG. - Experience with multi-agent orchestration, memory, and tool integrations. - Experience/implement MLOps practices and tools (CI/CD for ML, containerization, orchestration, model versioning, and reproducibility) Good to have. - Experience with cloud platforms (AWS, Azure, GCP) for scalable ML model deployment. - Good understanding of data pipelines, APIs, and distributed systems. - Build observability into AI systems latency, drift, performance metrics. - Strong written and verbal communication, presentation, client service, and technical writing skills in English for both technical and business audiences. - Strong analytical, problem-solving, and critical thinking skills. - Ability to work under tight timelines for multiple project deliveries. What we offer: At EY GDS, we support you in achieving your unique potential both personally and professionally. We give you stretching and rewarding experiences that keep you motivated, working in an atmosphere of integrity and teaming with some of the world's most successful companies. While we encourage you to take personal responsibility for your career, we support you in your professional development in every way we can. You enjoy the flexibility to devote time to what matters to you, in your business and personal lives. At EY, you can be who you are and express your point of view, energy, and enthusiasm, wherever you are in the world. It's how you make a difference. EY | Building a better working world: EY exists to build a better working world, helping to create long-term value for clients, people, and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform, and operate. Working across assurance, consulting, law, strategy, tax, and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.,

As a Lead AI/ML Researcher, your primary responsibility will be to spearhead the research, design, and development of cutting-edge AI and ML models that power an innovative AI-driven no-code development platform and a scalable AI inference and training orchestration system. Your role involves building scalable ML pipelines, optimizing models for production, mentoring team members, and translating research innovations into impactful product features that align with business objectives. You will be tasked with designing and implementing state-of-the-art machine learning and deep learning models for Natural Language Processing (NLP), computer vision, and generative AI that are relevant to the field of no-code AI coding and AI orchestration platforms. Additionally, you will develop, optimize, and fine-tune large-scale models, including transformer-based architectures and generative models. It will be crucial for you to architect and oversee end-to-end machine learning pipelines encompassing data processing, training, evaluation, deployment, and continuous monitoring. Collaboration with software engineering teams will be essential to ensure the successful productionization of models, focusing on factors such as reliability, scalability, and performance. Your role will also involve researching and integrating cutting-edge AI techniques and algorithms to uphold product competitiveness. Leading AI research efforts to contribute to intellectual property generation, patents, and academic publications will be a key aspect of your responsibilities. Moreover, you are expected to provide technical leadership and mentorship to junior AI/ML team members and collaborate cross-functionally with product managers, UX designers, and engineers to deliver AI-powered product features. Keeping abreast of AI research trends and technologies and evaluating their applicability, as well as ensuring compliance with data privacy and security standards in AI model development, will be integral to your role. Additionally, possessing experience with AI-driven no-code platforms, familiarity with AI workflow orchestration frameworks, knowledge of probabilistic modeling and uncertainty quantification, hands-on experience with MLOps tools and practices, familiarity with cloud platforms and container orchestration, contributions to open-source AI projects or patent filings, understanding of AI ethics and data privacy compliance, and a strong academic research background with publications in top-tier AI/ML conferences are considered advantageous skills. Qualifications for this role include a PhD in Computer Science, Electrical Engineering, Statistics, Mathematics, or related fields with a specialization in Artificial Intelligence, Machine Learning, or Deep Learning. A strong research publication record in reputable AI/ML conferences, demonstrated experience in developing and deploying deep learning models, proficiency in NLP and/or computer vision, hands-on experience with Python and ML frameworks, experience in building scalable ML pipelines, and knowledge of distributed training, GPU acceleration, and cloud infrastructure are highly desirable. Excellent problem-solving, analytical, and communication skills, along with prior experience in mentoring or leading junior AI researchers/engineers, will be beneficial for this position.,

As a DevOps Engineer at Emerson, you will be responsible for overseeing the end-to-end lifecycle of machine learning models, from deployment to monitoring and maintenance. You will work closely with data scientists, machine learning engineers, and development teams to ensure that ML models are efficiently integrated into production systems and deliver high performance. Your responsibilities in this role include deploying and handling machine learning models in production environments, designing and implementing CI/CD pipelines for ML models, developing and maintaining the infrastructure required for model deployment, supervising model performance, ensuring alignment with security and regulatory requirements, creating and maintaining documentation, identifying and implementing improvements, and participating in regular Scrum events. To excel in this role, you should possess a Bachelor's degree in computer science, Data Science, Statistics, or a related field, along with at least 7 years of experience in ML Ops, DevOps, or a related role. You should have expertise in deploying and handling machine learning models, experience with containerization technologies and orchestration platforms, familiarity with cloud services like Azure and AWS, and proficiency in CI/CD tools and practices. Preferred qualifications that set you apart include prior experience in the engineering domain and working with teams in Scaled Agile Framework (SAFe), knowledge of data engineering and ETL processes, experience with version control systems and collaboration tools, and understanding of machine learning model life cycle management. At Emerson, we prioritize a workplace where every employee is valued, respected, and empowered to grow. We foster innovation, collaboration, and diverse perspectives to drive growth and deliver business results. Our commitment to ongoing career development and inclusive culture ensures you have the support to thrive. We offer competitive benefits plans, medical insurance, Employee Assistance Program, flexible time off plans, and various leave options. Emerson is a global leader in automation technology and software, helping customers in critical industries operate sustainably while improving productivity and reliability. We offer equitable opportunities, celebrate diversity, and embrace challenges to make a positive impact across industries and countries. Join us at Emerson to contribute to vital work, develop your skills, and be empowered to think differently and make a difference.,

Role Overview: Zolvit is looking for a highly skilled and self-driven Lead Machine Learning Engineer / Lead Data Scientist to lead the design and development of scalable, production-grade ML systems. This role is ideal for someone who thrives on solving complex problems using data, is deeply passionate about machine learning, and has a strong understanding of both classical techniques and modern AI systems like Large Language Models (LLMs). You will work closely with engineering, product, and business teams to identify impactful ML use cases, build data pipelines, design training workflows, and ensure the deployment of robust, high-performance models at scale. Key Responsibilities: Design and implement scalable ML systems, from experimentation to deployment. Build and maintain end-to-end data pipelines for data ingestion, preprocessing, feature engineering, and monitoring. Lead the development and deployment of ML models across a variety of use cases including classical ML and LLM-based applications like summarization, classification, document understanding, and more. Define model training and evaluation pipelines, ensuring reproducibility and performance tracking. Apply statistical methods to interpret data, validate assumptions, and inform modeling decisions. Collaborate cross-functionally with engineers, data analysts, and product managers to solve high-impact business problems using ML. Ensure proper MLOps practices are in place for model versioning, monitoring, retraining, and performance management. Keep up-to-date with the latest advancements in AI/ML, and actively evaluate and incorporate LLM capabilities and frameworks into solutions. Mentor junior ML engineers and data scientists, and help scale the ML function across the organization. Required Qualifications: 7+ years of hands-on experience in ML/AI, building real-world ML systems at scale. Proven experience with classical ML algorithms (e.g., regression, classification, clustering, ensemble models). Deep expertise in modern LLM frameworks (e.g., OpenAI, HuggingFace, LangChain) and their integration into production workflows. Strong experience with Python, and frameworks such as Scikit-learn, TensorFlow, PyTorch, or equivalent. Solid background in statistics and the ability to apply statistical thinking to real-world problems. Experience with data engineering tools and platforms (e.g., Spark, Airflow, SQL, Pandas, AWS Glue, etc.). Familiarity with cloud services (AWS preferred) and containerization tools (Docker, Kubernetes) is a plus. Strong communication and leadership skills, with experience mentoring and guiding junior team members. Self-starter attitude with a bias for action and ability to thrive in fast-paced environments. Masters degree in Machine Learning, Artificial Intelligence, Statistics, or a related field is preferred. Preferred Qualifications: Experience deploying ML systems in microservices or event-driven architectures. Hands-on experience with vector databases, embeddings, and retrieval-augmented generation (RAG) systems. Understanding of Responsible AI principles and practices. Why Join Us? Lead the ML charter in a mission-driven company solving real-world challenges. Work on cutting-edge LLM use cases and platformize ML capabilities for scale. Collaborate with a passionate and technically strong team in a high-impact environment. Competitive compensation, flexible working model, and ample growth opportunities. Work Location - Bangalore & Chennnai Interested candidates, please share your resume to lakshmi@vakilsearch.com

Seeking an experienced AI Architect to lead the development of our AI and Machine Learning infrastructure and specialized language models. This role will establish and lead our MLOps practices and drive the creation of scalable, production-ready AI/ML systems. Key Responsibilities Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field

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