Data Scientist – ML Ops Engineer - Senior… at Deutsche Bank · Pune

Data Scientist – ML Ops Engineer - Senior Engineer, AS Location: Pune - Margarpatta Job Description: Job Title: Data Scientist – ML Ops Engineer - Senior Engineer, AS Location: Pune, India Role Description The Deutsche India is seeking a talented Data Scientist- ML Ops Engineer to join our growing team. At the “Service Solutions and AI” Domain, our mission is to revolutionize our Private Bank process landscape by implementing holistic, front-to-back process automation. As a Private Bank AI Centre of Excellence, we are responsible for strategy building and execution of AI innovation, governance, and delivery across Private Bank, ensuring standardization, compliance, and accelerated adoption of AI solutions. We are dedicated to leveraging the power of data and AI to drive innovation, optimize operations, and deliver exceptional value to our customers. We are committed to enhancing efficiency, agility, and innovation, with a keen focus on aligning every step of our process with the customer’s needs and expectations. Our dedication extends to driving innovative technologies, such as AI & workflow services, to foster continuous improvement. We aim to deliver “best in class” solutions across products, channels, brands, and regions, thereby transforming the way we serve our customers and setting new benchmarks in the industry. Join our innovative team as an MLOps Engineer - Data Science, where you will be instrumental in bridging the gap between cutting-edge machine learning model development and robust operational deployment. This hybrid role requires a basic understanding of data science / GenAI principles, machine learning algorithms, and MLOps best practices, with a significant emphasis on Generative AI, Large Language Models (LLMs), Agentic AI, and Transformer architectures. You will ensure that models are not only accurate and insightful but also robust, scalable, reliable, and continuously optimized in production environments. You will play a key role in designing, implementing, and maintaining the end-to-end machine learning lifecycle, from advanced data analysis and model building to deployment, monitoring, and continuous improvement, specifically focusing on the unique challenges and opportunities presented by Generative AI. What we’ll offer you As part of our flexible scheme, here are just some of the benefits that you’ll enjoy Best in class leave policy Gender neutral parental leaves 100% reimbursement under childcare assistance benefit (gender neutral) Sponsorship for Industry relevant certifications and education Employee Assistance Program for you and your family members Comprehensive Hospitalization Insurance for you and your dependents Accident and Term life Insurance Complementary Health screening for 35 yrs. And above Your key responsibilities Data Science & GenAI Model Development Knowledge: Basic Understanding and knowledge: Should have understanding and knowledge of the exploratory data analysis, sophisticated feature engineering, training, and testing of diverse machine learning models, including traditional ML and Generative AI models (LLMs, Agentic AI). This involves selecting MLOps method effectively. Operationalizing Data Analysis & Advanced Model Development: Strategic Data Exploration & Feature Engineering(Nice to have): Design and implement robust, reproducible pipelines for exploratory data analysis (EDA) and sophisticated feature engineering, ensuring data quality and readiness for both traditional ML and Generative AI models (LLMs, Agentic AI). This includes automating feature selection, transformation, and management via feature stores. Automated Model Training & Optimization: Develop and maintain automated workflows for building, training, and testing diverse machine learning models, including traditional ML and Generative AI models (LLMs, Agentic AI). This involves implementing continuous training (CT) loops, orchestrating hyperparameter tuning (e.g., Grid Search, Random Search, Bayesian Optimization), and managing experiment tracking for effective model iteration and optimization within the MLOps framework. MLOps & Generative AI Pipeline Automation: End-to-End Pipeline Design: Design, develop, and implement robust, end-to-end MLOps pipelines for continuous integration, continuous delivery (CI/CD), and continuous training (CT) of all machine learning models, with a special focus on orchestrating Generative AI models (LLMs, Agentic AI). Process Automation: Automate the entire ML lifecycle from data gathering and preparation to model training and deployment for both conventional and generative models. Model Deployment & Integration (with GenAI Focus): Scalable Deployment: Seamlessly deploy machine learning models, including complex LLMs and Agentic AI systems, into production environments, ensuring they are scalable, replicable, and accessible to applications and end-users. System Integration: Integrate ML and Generative AI models into larger enterprise systems, handling specific integration challenges of large-scale models. Model Monitoring & Maintenance (Generative AI Specific): Robust Monitoring Solutions: Implement robust monitoring solutions to track model performance, data drift, concept drift, and overall health in real-time for both traditional ML and Generative AI models. This includes specialized metrics for generative output quality. Proactive Troubleshooting: Troubleshoot and resolve critical issues related to model performance and deployment, ensuring continuous operational integrity for all deployed models, including advanced LLMs. Model Versioning & Governance (Generative AI Lifecycle): Best Practices & Governance: Establish and maintain best practices for model versioning, reproducibility, and comprehensive governance, specifically addressing the unique challenges of managing different versions of LLMs and Agentic AI systems. Cloud Infrastructure & MLOps Solutions: Infrastructure Management: Architect and manage the underlying infrastructure supporting ML and Generative AI models, leveraging containerization technologies (e.g., Docker, Kubernetes) and leading cloud platforms (e.g., AWS, Azure, GCP). Cloud MLOps Solutions: Drive the development and implementation of MLOps solutions tailored for cloud environments, optimizing for resource-intensive Generative AI workloads. Code Quality & Best Practices (Generative AI Engineering): Adherence to Standards: Write clean, maintainable, and well-documented code, strictly adhering to software development best practices and MLOps principles, especially for complex Generative AI implementations and orchestration frameworks like LangChain/LangGraph. Collaboration & Communication: Cross-Functional Collaboration: Collaborate closely and effectively with data scientists, ML engineers, software engineers, and IT operations teams to ensure seamless integration and operationalization of ML and Generative AI models in production. Stakeholder Communication: Communicate complex technical findings and intricate concepts, particularly those involving Generative AI, clearly and concisely to diverse technical and non-technical audiences. Research & Continuous Improvement (Generative AI Focus): Industry Research: Stay proactively updated with the latest advancements in machine learning, MLOps tools, cloud technologies, and specifically in Generative AI models, Agentic AI paradigms, RAG techniques, and orchestration frameworks. Innovative Solutioning: Propose and implement innovative solutions to optimize existing workflows and enhance overall MLOps capabilities, with a strategic eye on leveraging emerging Generative AI technologies. Your skills and experience Educational Foundation: Academic Background: Graduate’s, Master’s or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. Professional Expertise: Hybrid Role Experience: Proven experience (4+ years) in MLOps Engineer with strong data science fundamentals, including practical experience with Generative AI projects. Technical Mastery & Generative AI Specialization: Programming Proficiency: Expert-level proficiency in programming languages commonly used in data science and MLOps, particularly Python (with libraries such as scikit-learn, pandas, NumPy) and R, SQL. Core Data Science & ML Knowledge: Solid understanding and practical execution knowledge of statistical modeling, machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning), and experimental design. Generative AI Expertise: In-depth knowledge and hands-on experience with Generative AI concepts, Large Language Models (LLMs), Transformer architectures (including BERT), and Agentic AI principles. MLOps Proficiency: In-depth knowledge and hands-on experience with MLOps tools and frameworks (e.g., Kubeflow, MLflow, Airflow). Proven experience with designing and implementing CI/CD pipelines for ML models, specifically for LLMs and Generative AI applications. Advanced Orchestration: Practical experience with AgentOps, LangChain, and LangGraph for building and orchestrating complex multi-agent systems and AI workflows. RAG Implementation (Nice to have): Experience in designing and implementing Retrieval Augmented Generation (RAG) systems. Cloud & Container Technologies: Strong, demonstrable experience with leading cloud platforms (e.g., AWS, Azure, GCP) and services highly relevant to MLOps, including containerization (Docker) and orchestration (Kubernetes), optimized for Generative AI workloads. Model Deployment & Monitoring: Extensive experience with deploying ML models to production, architecting their monitoring, and setting up alerting for performance and operational issues, specifically including evaluation of Generative AI outputs. Data Engineering Fundamentals(Nice to have): Strong familiarity with data extraction and manipulation using SQL, and a solid understanding of data pipelines and data engineering principles essential for feeding and training Generative AI models. Problem-Solving & Strategic Competencies: Analytical & Problem-Solving Prowess: Excellent analytical and problem-solving abilities, capable of navigating complex systems and data challenges, and adept at troubleshooting intricate models and infrastructure issues, particularly in the evolving Generative AI landscape. Communication & Collaboration: Exceptional communication and collaboration skills to articulate complex technical details, work effectively with diverse cross-functional teams, and clearly explain complex concepts, including Generative AI implications, to stakeholders. Adaptability & Continuous Learning: Demonstrates strong adaptability to the fast-evolving nature of technology and business needs, with an unwavering commitment to continuous learning and improvement in the AI/MLOps landscape, particularly regarding Generative AI advancements. Domain-Specific Expertise (Preferred): Experience in the Banking & Finance Domain is preferred, understanding its unique data and regulatory landscape, and how Generative AI can be applied responsibly and effectively. How we’ll support you Training and development to help you excel in your career. Coaching and support from experts in your team. A culture of continuous learning to aid progression. A range of flexible benefits that you can tailor to suit your needs. About us and our teams Please visit our company website for further information: https://www.db.com/company/company.html We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively. Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group. We welcome applications from all people and promote a positive, fair and inclusive work environment.