1 day ago
Bengaluru, IndiaMid Level / Senior
Responsibilities
- Identify high-value AI use cases, assess feasibility, prototype solutions, and deliver successful pilot projects.
- Develop, fine-tune, deploy, and optimize AI models and large or small language models, including GPT-4 and open-source alternatives.
- Design prompt engineering strategies and build AI applications using Python and relevant frameworks.
- Integrate AI solutions with enterprise systems through API development and secure integrations.
- Build, maintain, and optimize data pipelines and manage structured and unstructured datasets for AI workloads.
- Use vector databases and semantic search to improve knowledge management capabilities.
- Deploy, manage, and scale AI solutions across Azure, AWS, and GCP cloud environments.
- Apply DevOps and MLOps practices for deployment, testing, monitoring, version control, and AI model lifecycle management.
- Ensure AI solutions meet security, privacy, compliance, ethical, and responsible-AI requirements, including GDPR and HIPAA considerations.
- Collaborate with clients, business teams, and internal stakeholders to translate requirements into technical solutions and measurable outcomes.
- Document technical designs, project plans, and operational procedures while contributing to AI best practices and internal frameworks.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.
- Typically 4–6 years of experience developing, deploying, and maintaining enterprise AI and machine learning solutions.
- Advanced experience with AI model development, fine-tuning, optimization, prompt engineering, and model reliability improvements.
- Advanced Python proficiency; JavaScript, TypeScript, Java, and C# experience is helpful or beneficial for interfaces and enterprise integrations.
- Experience with full-stack software development, frontend and backend integration, APIs, data pipelines, structured and unstructured data, and vector databases.
- Advanced experience deploying and scaling AI workloads in Azure, AWS, or GCP environments.
- Experience applying DevOps and MLOps practices to AI model lifecycles and deployments.
- Experience addressing security, privacy, compliance, risk management, ethical AI, bias mitigation, and responsible AI in regulated environments.
- Client and stakeholder engagement experience, business acumen, analytical problem-solving, organization, communication, and ability to manage multiple projects.
- Preferred certifications include Microsoft Certified: Azure AI Engineer Associate, Azure Solutions Architect Expert, Data Scientist Associate, Azure Data Engineer Associate, and Power Platform Fundamentals.
- Relevant certifications or training in machine learning, AI development, data analytics, and cloud computing are advantageous.
Benefits
- Hybrid working arrangement.
- NTT DATA offers a diverse and inclusive workplace with opportunities to grow, belong, and thrive.