5 Ai Deployment Jobs

As a Data Scientist specializing in Generative AI & ML Engineering, your primary responsibility will be to research and develop AI algorithms and models. You will be tasked with analyzing data, constructing predictive models, and employing machine learning techniques to address intricate problems. Your proficiency should encompass a range of skills including proficiency in languages/frameworks such as Fast API and Azure UI Search API (React), as well as expertise in databases and ETL tools like Cosmos DB and Data Factory Data Bricks. In addition, you should have a strong command of Python and R, familiarity with Azure Cloud Basics and Gitlab Pipeline, and experience in deploying AI solutions end-to-end. In addition to your proficient skills, you are expected to possess expert-level knowledge in areas such as Azure Open AI, Open AI GPT Family of models, and Azure Storage Account. Your expertise should extend to machine learning algorithms, deep learning frameworks like TensorFlow and PyTorch, and a solid foundation in mathematics including linear algebra, calculus, probability, and statistics. Furthermore, your role will require proficiency in data analysis tools such as Pandas, NumPy, and SQL, as well as strong statistical and probabilistic modeling skills. Experience with data visualization tools like Matplotlib, Seaborn, and Tableau, along with knowledge of big data technologies like Spark and Hive, will be essential for success in this position. Overall, your experience in AI-driven analytics and decision-making systems, coupled with your ability to develop and deploy AI frameworks and models, will be critical in delivering effective solutions to complex challenges.,

Responsibilities: * Design enterprise architectures for AI deployments, data lakes & warehouses. * Lead legacy system modernization initiatives. * Ensure compliance with NIST, ISO & GDPR standard * Align AI, cloud, and security with business goals.

Core Skills & Knowledge: Generative AI Expertise: Understanding of AI Models: Familiarity with various generative AI models like Large Language Models (LLMs), image generation models, and other relevant technologies. Prompt Engineering: Ability to craft effective prompts to guide AI models and achieve desired outcomes. AI Model Fine-Tuning: Knowledge of techniques to customize and improve AI models for specific applications. AI Deployment: Experience in deploying AI models into production environments. Software Development Proficiency: Programming Languages: Strong coding skills in languages like Python, JavaScript, Java, or similar. Software Architecture: Understanding of software architecture principles and design patterns. Databases: Knowledge of database technologies and their integration with AI models. Version Control: Proficiency in using version control systems like Git. Testing and Debugging: Ability to write unit tests, perform integration testing, and debug software issues. Agile Methodologies: Experience with agile development methodologies. Responsibilities : Developing AI-Powered Applications: Designing, developing, and deploying AI-powered applications and features. Integrating AI into Existing Systems: Seamlessly integrating AI technologies into existing software systems. Building AI Co-Pilots: Creating AI co-pilot solutions that augment human capabilities. Data Security and Privacy: Ensuring the responsible and ethical deployment of AI solutions. Collaboration: Working with cross-functional teams, including AI researchers, product managers, and other software engineers.

Drive strategic transformation roadmap for client accounts leveraging AI, Hyperautomation, Analytics Drive customer communication Contribute and present in client/customer reviews/meetings RFP support and defense Conduct goal setting with the team and ensure effective and measurable reviews and feedback mechanisms Follow appraisal and feedback mechanism Initiate and best practice sharing across the team Responsible for running improvement initiatives across the company as well as at client sites Identify possible improvement opportunities, make improvement plans, implement them, and ensure handover to operations Transfer knowledge and skills learned during expert training sessions to other employees Requirements: Robust F&A Transformation experience Prior Transformation / Consulting experience required AI deployment experience preferred Qualifications: Bachelor's Degree

Youll make a difference by: Siemens is seeking a visionary and technically strong Lead AI/ML Engineer to spearhead the development of intelligent systems that power the future of sustainable and connected transportation. This role will lead the design and deployment of AI/ML solutions across domains such as efficiency improvements in software development process, predictive maintenance, traffic analytics, computer vision for rail safety, and intelligent automation in rolling stock and rail infrastructure. Key Responsibilities Lead the end-to-end lifecycle of AI/ML projectsfrom data acquisition and model development to deployment and monitoringwithin the context of mobility systems. Architect scalable ML pipelines that integrate with Siemens Mobility platforms and other edge/cloud-based systems. Collaborate with multi-functional teams including domain experts, software architects, and system engineers to translate mobility use cases into AI-driven solutions. Mentor junior engineers and data scientists, and foster a culture of innovation, quality, and continuous improvement. Evaluate and integrate innovative research in AI/ML, including generative AI, computer vision, and time-series forecasting, into real-world applications. Ensure compliance with Siemens AI ethics, cybersecurity, and data governance standards. Required Qualifications Bachelor's or Masters or PhD in Computer Science, Machine Learning, Data Science, or a related field. 7+ years of experience in AI/ML engineering, with at least 2 years in a technical leadership role. Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. Proven experience deploying ML models in production, preferably in industrial or mobility environments. Familiarity with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (Azure, AWS, or GCP). Solid understanding of data engineering, model versioning, and CI/CD for ML. Preferred Qualifications Experience in transportation, automotive, or industrial automation domains. Knowledge of edge AI deployment, sensor fusion, or real-time analytics. Contributions to open-source AI/ML projects or published research. What We Offer Opportunity to shape the future of mobility through AI innovation. Access to Siemens global network of experts, labs, and digital platforms, flexible work arrangements, and continuous learning opportunities. A mission-driven environment focused on sustainability, safety, and digital transformation. Desired Skills: 9+ years of experience is required. Great Communication skills. Analytical and problem-solving skills