AWS AI Services

Project Role : AI Infrastructure Architect
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance power consumption cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation selection and full stack integration.
Must have skills : AWS AI Services
Good to have skills : Large Language Models (LLMs)
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing building integrating testing and operationalizing enterprise-grade LLM GenAI and agentic AI components across active client engagements.
Own platform-specific engineering on AWS translating high-level architecture into working production-quality components for LLM-driven applications RAG pipelines multi-agent workflows and AI platform integrations.
Bring practical industry experience in banking insurance healthcare retail telecom or capital markets to identify domain data process constraints controls and adoption risks while designing GenAI solutions that are safe scalable and relevant.
Operate as a hands-on Level 9 technical lead or Level 8 engineering lead contributing code design decisions reusable patterns and engineering documentation.
Key Responsibilities
Design and build LLM application components including prompts tools agents orchestration flows memory/context handling retrieval pipelines and evaluation harnesses.
Design agent workflows using Bedrock and serverless AWS patterns integrate enterprise APIs through Lambda and API Gateway build secure RAG over S3 OpenSearch and Knowledge Bases tune prompts and evaluation test suites for accuracy relevance faithfulness and safety.
Implement data ingestion parsing chunking enrichment embeddings vector search and retrieval workflows for structured and unstructured enterprise content.
Engineer safety and control components including PII detection/redaction prompt-injection defenses content filters guardrails authentication authorization lineage and audit logging.
Collaborate with architects data engineers product owners and security stakeholders to convert solution designs into tested observable and maintainable software components.
Maintain technical artifacts such as component designs integration specifications deployment runbooks evaluation results and reusable engineering patterns.
Required Qualifications
Bachelor s degree in Computer Science Computer Engineering Data Science AI/ML Information Technology or a related engineering discipline.
Level 9: typically 5 years of software/data/AI engineering experience including 2 years in cloud-native engineering and 1 year in GenAI LLM NLP or agentic AI delivery.
Level 8: typically 7 years of software/data/AI engineering experience including 3 years in cloud-native architecture/engineering and 12 years in GenAI LLM NLP or agentic AI delivery.
Hands-on coding experience in Python and strong understanding of APIs distributed systems CI/CD testing observability and secure SDLC practices.
Experience delivering AI/ML or data products in at least one industry domain such as banking insurance healthcare retail telecom or capital markets.
Required Skills/ Experience
Hands-on experience with Amazon Bedrock Bedrock Agents/AgentCore Knowledge Bases Guardrails Lambda API Gateway Step Functions OpenSearch Serverless/Vector Engine SageMaker IAM CloudWatch CloudTrail VPC KMS S3.
Strong understanding of LLM application architecture patterns including RAG function/tool calling agent orchestration model invocation prompt engineering embeddings vector databases and evaluation metrics.
Ability to implement traditional ML and GenAI components across ingestion feature/data preparation model integration deployment monitoring and continuous improvement.
Practical knowledge of security privacy governance performance scalability reliability and cost controls for production AI systems.
Experience with Git-based development automated testing CI/CD pipelines infrastructure-as-code and agile delivery in client-facing environments.
Good to Have Skills
AWS Solutions Architect or Machine Learning Specialty certification experience with CDK/Terraform EKS Bedrock model evaluation Amazon Q responsible AI controls and FinOps for GenAI workloads.
Exposure to open-source frameworks such as LangChain LangGraph LlamaIndex Haystack MLflow FastAPI Docker and Kubernetes.
Experience with Responsible AI model risk management synthetic data generation human-in-the-loop review A/B testing and GenAI cost optimization.