AI Infrastructure Architect - Large Language Models

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 technical lead or engineering lead, contributing code, design decisions, reusable patterns, and engineering documentation.

Responsibilities:

• Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost, and scalability of the computational stack. Advise on AI infrastructure technology and vendor evaluation, selection, and full-stack integration.
• 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 to integrate enterprise APIs through Lambda and API Gateway, build secure RAG over S3 OpenSearch, and knowledge bases, and 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.

Requirements:

• Bachelor's degree in computer science, computer engineering, data science, AI/ML, information technology, or a related engineering discipline.
• 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.
• 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, and 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.
• 15 years of full-time education.