**Introduction** A Technology Sales Engineer role (what we internally call a, 'Partner Technical Specialist') within IBM's Data & AI brand means accelerating enterprises' success by improving their ability to understand their data. It means providing solutions that enable people across organizations, in multiple roles, the ability to turn data into actionable insights without having to wait for IT. And it means selling multi-award winning software, and world-class design practices that enables business analysts to ask new questions. The answers to which are literally shaping the future and changing the world. Excellent onboarding and an industry leading learning culture will set you up for positive impact and success, whilst ongoing development will advance your career through an upward trajectory. Our sales environment is collaborative and experiential. Part of a team, you'll be surrounded by bright minds and keen co-creators - always willing to help and be helped - as you apply passion to work that will compel our clients to invest in IBM's products and services. **Your role and responsibilities** As a Partner Technical Specialist you'll work closely with clients to develop relationships, understand their needs, earn their trust and show them how IBM's industry leading solutions will solve their problems whilst delivering value to their business. Your primary responsibilities will include: * Client Strategy Design: Creating client strategies for Data & AI infrastructure. * Solution Definition: Defining IBM Data & AI solutions that enhance technology stacks. * Educational Support: Providing proof of concepts and simplifying complex topics to educate clients. * Credibility Building: Establishing credibility and trust to facilitate the closure of intricate Cloud tech deals. **Required technical and professional expertise** * Cutting-Edge Data Architecture (Lakehouse, Data Fabric) & Integration * * Engineer and optimize advanced data ecosystems that unify transactional and analytical workloads—leveraging Lakehouse concepts (e.g., Delta Lake, Databricks) to streamline data access. * Adopt Data Fabric or Data Mesh approaches for seamless data discovery, governance, and orchestration across distributed environments. * Deep ML & AI Expertise * * Own the end-to-end model lifecycle—from rapid prototyping in TensorFlow, or PyTorch to productionizing advanced AI techniques like NLP. * Continuously explore new frameworks and methodologies, pioneering innovative solutions that push Data and AI boundaries. * Proficiency in MLOps & Lifecycle Management * * Implement CI/CD pipelines with tools like MLflow or Kubeflow, ensuring automated model retraining, performance tracking, and timely updates. * Enabling ML solutions to deliver consistent, measurable outcomes solutions. * Cloud & Infrastructure Mastery (Highly Valued Plus) * * Leverage AWS, Azure, or Google Cloud to build secure, scalable AI solutions; utilize microservices, Docker, and Kubernetes for agility and efficiency. * Apply DevOps and DataOps best practices to accelerate deployments and optimize solution performance in production. * Hands-On Innovation & Consultative Solution Selling * * Rapidly build proofs-of-concept (POCs) to showcase real-world ROI, fueling stakeholder excitement and fast-tracking decisions. * Translate complex data and AI capabilities into compelling, results-oriented narratives—guiding clients from initial discovery to successful implementation. * Collaborate seamlessly with both technical and executive teams to ensure maximum ROI and enable transformative digital outcomes. **Preferred technical and professional experience** * Data Governance & Security Leadership * * Architect data solutions aligned with evolving regulations (GDPR, HIPAA, etc.), embedding robust governance into every layer of the data pipeline. * Promote ethical data usage and proactive risk management, instilling trust and transparency across all stakeholders. * Cloud & Infrastructure Mastery (Highly Valued Plus) * * Leverage AWS, Azure, or Google Cloud to build secure, scalable AI solutions; utilize microservices, Docker, and Kubernetes for agility and efficiency. * Apply DevOps and DataOps best practices to accelerate deployments and optimize solution performance in production. IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.