AI Technology and Innovation Engineer at NTT Ltd.

Your day at NTT DATA

• The AI Solutions Engineer is an advanced subject matter expert, responsible for actively participating in identifying high-value use cases, assessing solution feasibility, prototyping innovative ideas, and delivering successful pilot projects. 
• This role demands specialist technical knowledge, proficiency in software engineering, and capability in data engineering and cloud infrastructure.
• The AI Solutions Engineer is expected to ensure secure, reliable, and scalable integrations with existing enterprise platforms, systems, and data sources.

Key Responsibilities:

• Develop, fine-tune, and deploy AI models, including large language models (LLMs) such as GPT-4 or open-source equivalents.
• Design and implement effective prompt engineering strategies and optimizations to enhance AI accuracy, consistency, and reliability.
• Engage with internal stakeholders and clients to understand business needs, translating them into actionable AI solutions.
• Rapidly prototype, test, and iterate AI applications using advanced Python programming and relevant frameworks.
• Integrate AI solutions securely with existing enterprise systems (CRM, ERP, HRIS, finance platforms, collaboration software) via API development and integration.
• Build, maintain, and optimize end-to-end data pipelines to ensure accurate and timely data delivery for AI models.
• Manage structured and unstructured datasets, leveraging vector databases and semantic search to enhance knowledge management capabilities.
• Deploy, manage, and scale AI solutions within cloud computing environments (Azure, AWS, GCP), ensuring high availability, performance, and cost efficiency.
• Implement DevOps and MLOps practices, including automated deployment, testing, monitoring, and version control, to efficiently manage the AI model lifecycle.
• Ensure AI solutions adhere to industry standards and compliance regulations (GDPR, HIPAA), emphasizing security and privacy best practices.
• Identify and mitigate risks associated with AI deployments, proactively addressing ethical considerations, biases, and unintended consequences.
• Collaborate closely with business and functional teams to streamline processes through intelligent automation and deliver measurable business outcomes.
• Provide clear documentation of technical designs, project plans, and operational procedures.
• Contribute to the continuous improvement of AI best practices, methodologies, and internal frameworks.
• Stay abreast of the latest AI and machine learning developments, continuously evaluating emerging technologies and methodologies.

To thrive in this role, you need to have:

• Advanced understanding of artificial intelligence, natural language processing (NLP), and machine learning principles.
• Advanced expertise in selecting, fine-tuning, and deploying large and small language models (LLMs/SLMs), such as OpenAI’s GPT series and open-source alternatives.
• Advanced proven experience with prompt engineering, prompt optimization, and AI model reliability and accuracy improvements.
• Advanced proficiency in Python programming, essential for rapid prototyping, integration, and model implementation. Python is the preferred language for AI; strong proficiency in Python is essential due to the extensive use of frameworks, libraries, and models.
• Advanced knowledge of additional programming languages (optional, but valuable):
• JavaScript / TypeScript: Helpful if building frontend interfaces or web integrations.
• Java / C#: Beneficial for integrations with enterprise backend systems (e.g., ERP, CRM).
• Advanced familiarity with full-stack software development, including frontend and backend integration, user experience considerations, and system interoperability.
• Robust knowledge of data pipeline development, data engineering concepts, and handling of structured and unstructured data.
• Advanced proficiency in cloud computing platforms (Azure, AWS, GCP), particularly in deploying, scaling, and managing AI workloads.
• Advanced knowledge of security, compliance, and risk management practices related to AI solutions.
• Advanced understanding of ethical AI considerations, bias mitigation, and responsible AI deployment.
• Demonstrated domain and business acumen, capable of aligning technical solutions with business strategies and processes for measurable impact.
• Excellent communication skills, ability to clearly articulate technical concepts to non-technical stakeholders.
• Advanced analytical problem-solving capabilities, organizational skills, and attention to detail.
• Ability to manage multiple projects simultaneously, prioritize tasks effectively, and meet deadlines in a fast-paced environment.
• Passionate about continuous learning, innovation, and keeping abreast of industry trends.

Academic Qualifications and Certifications:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.
• Microsoft Certified: Azure AI Engineer Associate (preferred)
• Microsoft Certified: Azure Solutions Architect Expert (preferred)
• Microsoft Certified: Data Scientist Associate (preferred)
• Microsoft Certified: Azure Data Engineer Associate (preferred)
• Microsoft Certified: Power Platform Fundamentals (preferred)
• Relevant certifications or training in Machine Learning, AI development, Data Analytics, and Cloud Computing (advantageous)

Required Experience:

• Advanced demonstrated experience (typically 4-6 years) developing, deploying, and maintaining AI and machine learning solutions in enterprise environments.
• Advanced expertise in AI model development, fine-tuning, and optimization using Python and relevant frameworks.
• Advanced demonstrated experience implementing prompt engineering methodologies and optimizing model performance.
• Advanced demonstrated experience in API development and secure integration of AI-driven solutions with enterprise systems and platforms.
• Advanced experience building robust data pipelines, managing structured/unstructured data, and leveraging vector databases.
• Practical experience deploying and scaling AI applications within cloud platforms (Azure, AWS, or GCP).
• Advanced demonstrated success applying DevOps and MLOps best practices to manage AI model lifecycle and deployments efficiently.
• Advanced proven track record ensuring security, privacy, compliance, and responsible use of AI solutions within regulated environments.
• Advanced experience engaging directly with clients and stakeholders, translating business requirements into effective technical solutions.
• AI experience - ChatGPT, Microsoft Copilot, Gemini, and/or Claude