A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.
You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio; including Software and Red Hat.
Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in ground breaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment that embraces your unique skills and experience.
Who you are: A seasoned Data Scientist specializing in Artificial Intelligence (AI) and Machine Learning (ML), with deep expertise in developing scalable AI-driven solutions for large enterprise environments. Having hands-on experience in ML model development, deep learning, NLP, and predictive analytics and are proficient in deploying AI models on enterprise data platforms.
Ability to work with big data, optimize ML pipelines, and integrate AI solutions into business processes makes you a key contributor to driving data-driven transformation. Excel at collaborating with cross-functional teams, bridging the gap between AI research and real-world applications.What you’ll do: As a Data Scientist – Artificial Intelligence, you will be responsible for:
AI & Machine Learning Model Development
• Designing, developing, and optimizing ML and AI models for predictive analytics, anomaly detection, and automation.
• Implementing NLP models, generative AI techniques, and recommendation systems to enhance data insights.
• Utilizing deep learning frameworks (TensorFlow, PyTorch, Keras) for advanced AI solutions.
Big Data & Model Deployment
• Deploying AI/ML models on Cloudera Machine Learning (CML) and optimizing them for performance and scalability.
• Leveraging Apache Spark, PySpark, and distributed computing for handling large-scale data processing.
• Integrating AI models with Cloudera Data Platform (CDP), Kafka, and Apache Iceberg for real-time analytics.
AI for Data Quality & Governance
• Implementing AI-powered data quality monitoring using Talend Data Quality (DQ) and anomaly detection models.
• Enhancing data lineage tracking and metadata management using AI-driven insights.
• Collaborating with Denodo for AI-based data virtualization and automated data mapping.
Security & Compliance
• Ensuring AI solutions comply with Bank/Regulatory security and governance policies.
• Implementing AI-driven encryption techniques using Thales CipherTrust for secure model inference.
Collaboration & Leadership
• Partnering with business leaders, architects, and data engineers to align AI strategies with business goals.
• Leading AI-driven innovation initiatives, mentoring junior data scientists, and fostering a data-driven culture.
• Working with GitLab CI/CD pipelines, Sonatype Nexus, and CheckMarx to ensure secure and scalable AI model deployments.
10+ years of experience in AI, Machine Learning, and Data Science.
• Strong expertise in Python, R, SQL, and ML/DL frameworks (TensorFlow, PyTorch, Scikit-Learn).
• Experience deploying AI/ML models on Cloudera Machine Learning (CML) and Apache Spark.
• Proficiency in NLP, generative AI, and deep learning techniques.
• Hands-on experience with big data technologies (Kafka, Iceberg format, Cloudera Data Platform).
• Strong knowledge of data security, encryption, and compliance in AI workflows.
• Experience working with Denodo, Talend DQ, and graph databases (DGraph Enterprise).
• Proven leadership in mentoring, strategic AI implementation, and stakeholder collaboration.
Experience in AI-driven data migration projects for Banking domain.
• Knowledge of AI ethics, explainable AI (XAI), and responsible AI frameworks.
• Experience with cloud-based AI platforms (AWS Sagemaker, Azure ML, or GCP Vertex AI).
• Familiarity with MLOps, CI/CD for ML models, and AI-driven automation.
• Certifications in IBM AI Engineering, Cloudera Data Science, or Google/AWS AI solutions.
• Exposure to ‘Meghdoot’ cloud platform.