Jobs via Dice
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 such as 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‑tune pre‑trained models (e.g., GPT, BERT) and optimize prompt engineering techniques to deliver high‑quality, actionable outputs for diverse business use cases.
Perform embeddings generation, evaluate outputs, and incorporate human/automated feedback loops.
Apply advanced NLP techniques such as tokenization, prompt engineering, and query optimization.
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.
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.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industry Software Development
#J-18808-Ljbffr
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 such as 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‑tune pre‑trained models (e.g., GPT, BERT) and optimize prompt engineering techniques to deliver high‑quality, actionable outputs for diverse business use cases.
Perform embeddings generation, evaluate outputs, and incorporate human/automated feedback loops.
Apply advanced NLP techniques such as tokenization, prompt engineering, and query optimization.
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.
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.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industry Software Development
#J-18808-Ljbffr