TECHNICAL ARCHITECT AI
Purple Drive, Santa Clarita, CA, United States
Overview:
Position Overview
We are seeking a highly skilled
Technical Architect (AI)
to join our team. In this role, you will leverage your expertise in
Generative AI, LLMs, NLP, and machine learning
to design and implement innovative AI-driven solutions. The ideal candidate will have a deep understanding of
architectural design, RAG pipelines, AI agents, guardrails, and the latest advancements in large language models . You will play a key role in delivering cutting-edge solutions, working with large-scale data, and building systems that enhance automation, intelligence, and efficiency for our clients.
Key Responsibilities
AI Solutioning & Architecture
Lead the design and implementation of
end-to-end AI solutions
ensuring scalability, robustness, and efficiency aligned with business needs.
Architect
RAG pipelines
using frameworks like
LangChain, LlamaIndex, or custom-built stacks .
Design
Agentic AI architectures , including
task-based agents, stateful memory, planning-execution workflows, and tool augmentation .
Data Strategy & AI Model Development
Define and execute
data strategies
for collection, cleaning, transformation, and integration.
Fine-tuning & Prompt Engineering:
Fine-tuning pre-trained models (e.g., GPT, BERT, etc.) and optimize prompt engineering techniques to drive high-quality, actionable outputs for diverse business use cases.
Perform
embeddings generation, evaluation of outputs , and incorporate
human/automated feedback loops .
Apply
advanced NLP techniques
such as tokenization, prompt engineering, and query optimization.
Machine Learning & Deep Learning Models:
Build, train, and deploy machine learning models, including deep learning models, for complex AI applications across various domains.
AI Guardrails & Safety
Build and enforce
guardrails
for model safety and compliance, including
prompt validation, output moderation, and access controls .
Ensure solutions meet
data governance, compliance, and security standards .
Deployment & Cloud-Native Enablement
Collaborate with teams to deploy solutions in
AWS cloud-native environments
(Bedrock, Lambda, ECS, SageMaker, CDK).
Oversee
CI/CD pipelines, API integrations, and scalable production deployments .
Lead
LLM provisioning from AWS , balancing performance and cost-effectiveness.
Deployment & Evaluation: Oversee the deployment of AI models, ensuring smooth integration with production systems, and perform rigorous evaluation of LLMs for accuracy, efficiency, and scalability.
Observability & post-deployment
Contribute to
system observability.
Support
post-deployment monitoring, optimization, and retraining cycles
for LLM-driven systems.
Technologies & Frameworks
LLM:
Expertise in AWS Bedrock
RAG:
LangChain, LlamaIndex, CrewAI, VectorDB
Programming:
Python
Cloud Platforms:
AWS (Bedrock, SageMaker, Lambda, CDK)
Data & Databases:
SQL, NoSQL, Data Lakes, Data Warehouses.
Orchestration & Deployment:
CI/CD pipelines, containerized microservices, Kubernetes.
Required Skills & Qualifications
Proven
production experience with RAG pipelines
(LangChain, LlamaIndex, or custom stacks).
Strong understanding of
Agentic AI patterns : task agents, memory/state tracking, orchestration.
Expertise in
LLM fine-tuning, embeddings, evaluation strategies, and feedback integration .
Hands-on experience with
AI guardrails
(moderation, filtering, prompt validation).
Proficiency in
Python, vector DBs, and LLM APIs
.
Familiarity with
CI/CD, API integration, and cloud-native deployments .
Strong database management skills (SQL & NoSQL).
Excellent
communication, solutioning, and leadership
capabilities.
Position Overview
We are seeking a highly skilled
Technical Architect (AI)
to join our team. In this role, you will leverage your expertise in
Generative AI, LLMs, NLP, and machine learning
to design and implement innovative AI-driven solutions. The ideal candidate will have a deep understanding of
architectural design, RAG pipelines, AI agents, guardrails, and the latest advancements in large language models . You will play a key role in delivering cutting-edge solutions, working with large-scale data, and building systems that enhance automation, intelligence, and efficiency for our clients.
Key Responsibilities
AI Solutioning & Architecture
Lead the design and implementation of
end-to-end AI solutions
ensuring scalability, robustness, and efficiency aligned with business needs.
Architect
RAG pipelines
using frameworks like
LangChain, LlamaIndex, or custom-built stacks .
Design
Agentic AI architectures , including
task-based agents, stateful memory, planning-execution workflows, and tool augmentation .
Data Strategy & AI Model Development
Define and execute
data strategies
for collection, cleaning, transformation, and integration.
Fine-tuning & Prompt Engineering:
Fine-tuning pre-trained models (e.g., GPT, BERT, etc.) and optimize prompt engineering techniques to drive high-quality, actionable outputs for diverse business use cases.
Perform
embeddings generation, evaluation of outputs , and incorporate
human/automated feedback loops .
Apply
advanced NLP techniques
such as tokenization, prompt engineering, and query optimization.
Machine Learning & Deep Learning Models:
Build, train, and deploy machine learning models, including deep learning models, for complex AI applications across various domains.
AI Guardrails & Safety
Build and enforce
guardrails
for model safety and compliance, including
prompt validation, output moderation, and access controls .
Ensure solutions meet
data governance, compliance, and security standards .
Deployment & Cloud-Native Enablement
Collaborate with teams to deploy solutions in
AWS cloud-native environments
(Bedrock, Lambda, ECS, SageMaker, CDK).
Oversee
CI/CD pipelines, API integrations, and scalable production deployments .
Lead
LLM provisioning from AWS , balancing performance and cost-effectiveness.
Deployment & Evaluation: Oversee the deployment of AI models, ensuring smooth integration with production systems, and perform rigorous evaluation of LLMs for accuracy, efficiency, and scalability.
Observability & post-deployment
Contribute to
system observability.
Support
post-deployment monitoring, optimization, and retraining cycles
for LLM-driven systems.
Technologies & Frameworks
LLM:
Expertise in AWS Bedrock
RAG:
LangChain, LlamaIndex, CrewAI, VectorDB
Programming:
Python
Cloud Platforms:
AWS (Bedrock, SageMaker, Lambda, CDK)
Data & Databases:
SQL, NoSQL, Data Lakes, Data Warehouses.
Orchestration & Deployment:
CI/CD pipelines, containerized microservices, Kubernetes.
Required Skills & Qualifications
Proven
production experience with RAG pipelines
(LangChain, LlamaIndex, or custom stacks).
Strong understanding of
Agentic AI patterns : task agents, memory/state tracking, orchestration.
Expertise in
LLM fine-tuning, embeddings, evaluation strategies, and feedback integration .
Hands-on experience with
AI guardrails
(moderation, filtering, prompt validation).
Proficiency in
Python, vector DBs, and LLM APIs
.
Familiarity with
CI/CD, API integration, and cloud-native deployments .
Strong database management skills (SQL & NoSQL).
Excellent
communication, solutioning, and leadership
capabilities.