AWS Senior AI Engineer at NTT DATA, Inc. in Bangalore, IN-KA, India

Job description

• As a Senior AI Engineer at NTT DATA in Bangalore, you will be responsible for designing, developing, and deploying enterprise AI applications using AWS cloud technologies. The role involves building Generative AI, Conversational AI, and Agentic AI solutions, integrating LLMs, and developing reusable AI frameworks. You will work closely with architects, product owners, and business stakeholders to deliver scalable, secure, and AI-driven business outcomes. The position requires at least 8 years of software engineering experience, 3 years in AI or ML development, strong proficiency in Python, and expertise in AWS AI services. A bachelor’s degree in a relevant field is mandatory. Preferred qualifications include a master’s degree, AWS certifications, and experience with MLOps, containerisation, and enterprise-scale AI transformation programmes.
• Req ID: 386198
• NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
• We are currently seeking a AWS Senior AI Engineer to join our team in Bangalore, Karnātaka (IN-KA), India (IN).
• We are seeking an experienced Senior AI Engineer / Developer to develop, deploy, and optimize enterprise grade AI solutions using AWS cloud technologies, Generative AI, Large Language Models (LLMs), Agentic AI, and intelligent automation capabilities.
• The successful candidate will work closely with AI Architects, Product Owners, Business Stakeholders, and Engineering teams to build scalable AI powered applications and intelligent business solutions. This role requires strong expertise in cloud native development, AI application engineering, model integration, and production deployment of AI solutions.
• The ideal candidate combines software engineering excellence with deep expertise in Generative AI, AWS AI/ML services, AI agents, open source LLMs, and modern AIOps practices.
• A successful candidate is a hands on Senior AI Engineer who can take AI solutions from concept to production. They possess strong expertise in AWS AI services, Amazon Bedrock, Amazon Q, Generative AI, Open Source LLMs, Agentic AI, and cloud native application development, while demonstrating the ability to build secure, scalable, high performance AI solutions that drive tangible business outcomes.

Responsibilities

• AI Solution Development
• • Design, develop, and deploy enterprise AI applications using AWS services and modern AI frameworks.
• • Build Generative AI, Conversational AI, AI Assistant, and Agentic AI solutions.
• • Translate business requirements into scalable, resilient, and secure AI applications.
• • Develop reusable AI frameworks, APIs, integration services, and accelerators.
• • Collaborate with architects and business stakeholders to deliver AI driven business outcomes.
• Generative AI & Agentic AI Development
• • Build applications leveraging foundation models and Large Language Models (LLMs).
• • Design and implement Retrieval Augmented Generation (RAG) architectures using enterprise knowledge sources.
• • Develop AI agents and multi agent orchestration workflows.
• • Implement prompt engineering, context management, memory patterns, and evaluation frameworks.
• • Evaluate and integrate commercial and open source models based on performance, cost, scalability, and security requirements.
• • Develop AI powered assistants capable of automating business processes and decisionmaking workflows.
• AWS AI & Cloud Development
• • Design and develop AI solutions leveraging Amazon Bedrock, Amazon SageMaker, Amazon Q, OpenSearch, Lambda, ECS, EKS, and related AWS services.
• • Develop cloud native AI applications utilizing serverless and container based architectures.
• • Build scalable APIs and microservices supporting AI workloads.
• • Integrate AI solutions with enterprise applications, business systems, and data platforms.
• • Ensure high availability, security, reliability, observability, and operational excellence.
• Intelligent Automation & Business Process Integration
• • Design AI powered workflow automation solutions integrating AWS AI capabilities with enterprise systems.
• • Build intelligent process automation solutions leveraging AI services, APIs, and workflow orchestration tools.
• • Integrate AI capabilities into business applications to improve operational efficiency and employee productivity.
• • Develop reusable automation frameworks and enterprise integration patterns.
• Open Source AI & Model Engineering
• • Deploy and optimize open source foundation models including Llama, Mistral, and similar models.
• • Fine tune foundation models for business specific use cases.
• • Build and manage model serving and inference environments.
• • Support model lifecycle management, governance, monitoring, testing, and version control.
• Model Optimization & Performance Engineering
• • Optimize AI solutions for latency, throughput, scalability, and operational efficiency.
• • Apply quantization, model compression, distillation, pruning, and inference optimization techniques.
• • Optimize GPU utilization and infrastructure performance.
• • Design cost efficient AI architectures balancing performance, business value, and cloud spend.
• • Monitor AI applications and continuously improve model effectiveness and reliability.
• DevOps, MLOps & AI Operations
• • Implement CI/CD pipelines supporting AI application delivery.
• • Build MLOps and LLMOps processes for model deployment, testing, monitoring, and governance.
• • Support production operations and troubleshooting of enterprise AI solutions.
• • Ensure compliance with security, Responsible AI, and enterprise governance standards.
• Collaboration & Technical Leadership
• • Collaborate with AI Architects, Data Scientists, Product Owners, Developers, and Business Stakeholders.
• • Participate in solution design, architecture reviews, and code reviews.
• • Mentor junior engineers and promote AI engineering best practices.
• • Stay current with emerging AI technologies, tools, frameworks, and industry trends.
• • Support client demonstrations, workshops, technical proposals, and innovation initiatives.

Requirements

• • Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field.
• • 8+ years of software engineering or application development experience.
• • 3+ years of hands on experience developing AI, Machine Learning, or Generative AI solutions.
• • Strong proficiency in Python and modern software engineering practices.
• • Experience building cloud native applications on AWS.
• • Strong understanding of API development, microservices, distributed systems, and cloud architectures.
• • Experience integrating LLMs and Generative AI capabilities into enterprise applications.
• • Experience deploying and managing AI workloads in production environments.
• • Strong communication, stakeholder management, and problem solving skills.
• • Master's degree in Artificial Intelligence, Computer Science, Data Science, or related discipline.
• • AWS Certified and Experience with Amazon Bedrock and Amazon Q.
• • Experience with MLOps, LLMOps, and AI platform engineering.
• • Experience deploying containerized workloads using Docker and Kubernetes.
• • Experience working on enterprise scale AI transformation programs.
• • Experience in customer facing consulting or solution delivery roles.

Skills

Required

• Strong proficiency in Python
• Modern software engineering practices
• API development
• Microservices
• Distributed systems
• Cloud architectures
• AWS cloud native development
• LLM integration
• Generative AI
• Production deployment of AI workloads

Preferred

• AWS Certified
• Amazon Bedrock
• Amazon Q
• MLOps
• LLMOps
• AI platform engineering
• Docker
• Kubernetes