About this role
The AWS AI Engineer develops, deploys and optimizes enterprise AI applications on AWS. Working with AI architects, product owners, engineers and business stakeholders, the role takes secure, scalable AI solutions from business requirements through production. This is a full-time position in Bangalore, Karnātaka, India, with 40 hours per week.
Responsibilities
- Build generative AI, conversational AI, assistant and agentic applications; create reusable frameworks, APIs, integration services and automation patterns for enterprise systems and business workflows.
- Integrate foundation models and LLMs; design retrieval-augmented generation using enterprise knowledge, multi-agent orchestration, prompts, context and memory patterns, and evaluation frameworks. Assess commercial and open-source models for performance, cost, scalability and security.
- Develop cloud-native AI applications with Amazon Bedrock, SageMaker, Amazon Q, OpenSearch, Lambda, ECS and EKS, using serverless or container-based architectures. Build APIs and microservices and integrate them with enterprise applications and data platforms.
- Deploy and fine-tune open-source models such as Llama and Mistral; manage serving, inference, model lifecycle, testing, version control and monitoring. Improve latency, throughput and cost through quantization, compression, distillation, pruning and GPU optimization.
- Implement CI/CD, MLOps and LLMOps for production deployment and support. Maintain availability, observability, security, reliability, responsible AI practices and enterprise governance. Participate in design and code reviews, mentor junior engineers, and support client demonstrations, workshops and technical proposals.
Required qualifications
A bachelor’s degree in Computer Science, Engineering, Information Technology, Data Science or a related field; 5+ years of software engineering or application development experience; and 2+ years of hands-on AI, machine learning or generative AI development. Candidates need strong Python skills, AWS cloud-native development experience, knowledge of APIs, microservices and distributed systems, and experience integrating LLMs into enterprise applications and operating AI workloads in production. Communication, stakeholder management and problem-solving skills are required.
Preferred qualifications
A relevant master’s degree, AWS certification and experience with Bedrock and Amazon Q; MLOps, LLMOps or AI platform engineering; Docker and Kubernetes; enterprise-scale AI transformation; and customer-facing consulting or solution delivery. NTT DATA states that remote or hybrid options vary by position and client needs, and office or client-site attendance may be required.