AI & LLM Architect

Experience Level: Lead (8-10 years)

We are seeking a highly skilled and hands-on AI Architect to lead the design and implementation of next-generation enterprise AI platforms powered by Large Language Models (LLMs) and advanced agentic architectures. The ideal candidate will possess deep expertise in scalable AI system design, AWS-native AI services, multi-agent orchestration, LLM evaluation frameworks, guardrails, and performance optimization.

This role requires a unique combination of advanced technical leadership, architectural vision, and hands-on engineering expertise to build secure, scalable, production-grade AI solutions that support complex enterprise workflows.

Enterprise AI & LLM Architecture

Architect and deliver enterprise-scale AI platforms leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), fine-tuning strategies, and advanced agentic AI architectures.

Design and implement multi-agent systems capable of:

Tool orchestration

Autonomous planning and reasoning

Context and memory management

Distributed workflow execution

Define scalable AI architecture patterns supporting high availability, security, observability, and enterprise governance.

Lead the integration of AI systems into enterprise applications, APIs, and cloud-native ecosystems.

LLM Guardrails & Responsible AI

Design and implement robust guardrails for enterprise LLM applications, including:

Content filtering

Prompt injection mitigation

Hallucination reduction

Policy enforcement and compliance controls

Implement responsible AI practices using:

Safety frameworks

Human-in-the-loop validation

Governance and audit mechanisms

Ensure ethical, compliant, reliable, and secure AI usage across enterprise environments.

LLM Cost Optimization & Performance Engineering

Optimize LLM workloads for:

Cost efficiency

Low latency

High throughput

Operational scalability

Implement:

Model selection strategies

Prompt optimization techniques

Token usage optimization

Response caching and batching mechanisms

Design scalable inference architectures and usage monitoring frameworks to balance performance with operational cost.

Evaluation Frameworks & Model Quality Engineering (Evals)

Develop and operationalize comprehensive evaluation frameworks for LLM applications, including:

Automated benchmarking

Prompt evaluation

Response scoring

Regression testing

Implement both offline and online evaluation methodologies, including:

Human evaluation pipelines

A/B testing frameworks

Continuous feedback loops

Continuously improve model quality, reliability, and production performance using measurable evaluation metrics.

AWS Bedrock & Agentic Frameworks

Hands-on expertise with Amazon Bedrock, including:

Foundation model evaluation

Prompt engineering

AI orchestration

Model lifecycle management

Experience with AgentCore or equivalent agentic frameworks for building:

Stateful AI agents

Tool-augmented agents

Memory-enabled agent systems

Controlled execution workflows

Design composable AI services using cloud-native orchestration patterns.

Advanced Python & API Engineering

PyTorch

TensorFlow

Hugging Face

LangChain or equivalent frameworks

Build scalable, production-grade APIs using FastAPI, including:

Async processing

AI model inference optimization

High-performance microservices architecture

Develop reusable AI service components and distributed API integrations.

CI/CD and MLOps for AI Systems

Automated model testing

Validation workflows

Reproducibility controls

Staged deployments and rollbacks

Implement MLOps best practices using:

Model versioning

Feature stores

Continuous monitoring

Automated retraining workflows

Experience with tools and platforms such as:

GitHub Actions

Jenkins

AWS CodePipeline

Terraform

CloudFormation

AWS Cloud Architecture

Design secure and scalable AWS cloud architectures for AI workloads.

Strong understanding of Amazon VPC architecture, including:

Subnetting strategies

Private networking

Security segmentation

High-availability design

Hands-on experience with:

AWS Lambda

API Gateway

SQS

Step Functions

Event-driven serverless architectures

Build scalable inference pipelines and cloud-native integrations across AWS services.

Distributed Team Leadership & Global Delivery

Lead distributed AI engineering and platform development teams across multiple time zones.

Drive high productivity, technical alignment, and delivery excellence across globally distributed teams.

Implement agile delivery methodologies and scalable engineering processes.

Collaborate cross-functionally with architecture, engineering, product, security, and operations teams.

Required Qualifications

8+ years of overall software engineering experience

4+ years of AI/ML architecture experience

Deep expertise in LLM ecosystems, RAG pipelines, and agentic AI architectures

Strong hands-on experience with AWS-native AI services and cloud platforms

Expertise in Python-based AI application development

Experience building scalable enterprise AI systems in production environments

Strong understanding of AI governance, responsible AI, and evaluation methodologies

Excellent communication, architectural, and leadership skills

Preferred Qualifications

Experience working with enterprise AI governance frameworks

Knowledge of vector databases and semantic search architectures

Familiarity with distributed systems and event-driven microservices

Experience supporting globally distributed engineering organizations

AWS certifications or AI/ML specialization certifications preferred

Top 5 Must-Have Skills