SEED AI Architect

Manchester, CT - USA

Monthly Salary: Not Disclosed

Posted on: 9 hours ago

Vacancies: 1 Vacancy

Job Summary

AI / GenAI

Strong experience with Generative AI and Agentic AI architectures

Hands on knowledge of LLMs embeddings RAG pipelines prompt engineering and agent frameworks

Proficiency in Python for AI/ML development

ML & Data Engineering

Experience with ML/DL frameworks such as TensorFlow PyTorch scikit learn

Knowledge of data engineering feature engineering and analytics pipelines

Familiarity with vector databases graph databases and search engines

Cloud & Platform

Experience designing AI solutions on cloud platforms (AWS Azure or GCP)

Strong understanding of cloud native microservices and API driven architectures

Exposure to observability monitoring and logging for AI systems

Security & Compliance

Knowledge of data privacy security best practices and enterprise compliance standards

Experience designing secure and governed AI solutions

Roles & Responsibilities

AI & Solution Architecture

Define and own end to end AI architecture for enterprise solutions from data ingestion to AI driven decisioning and insights.

Design AI native platforms where GenAI and ML capabilities are embedded as core services not bolt ons.

Establish modular reusable and composable AI components aligned to enterprise architecture standards.

Ensure scalability performance reliability and security of AI systems in production.

GenAI & Agentic AI

Architect Generative AI solutions including:

o Large Language Models (LLMs)

o Retrieval Augmented Generation (RAG)

o Multi agent and agent orchestration patterns

Define agent workflows memory/context handling tool integration and decision confidence mechanisms.

Guide responsible selection and usage of cloud based and open source LLMs.

Data ML & MLOps

Design AI solutions leveraging modern data platforms feature stores vector databases and knowledge graphs.

Define MLOps / LLMOps pipelines for training evaluation deployment monitoring and lifecycle management.

Implement mechanisms for model versioning drift detection cost optimization and continuous improvement.

Governance Security & Responsible AI

Ensure AI solutions adhere to enterprise security privacy and compliance requirements.

Embed Responsible AI principles including explainability auditability bias mitigation and human in the loop controls.

Define governance frameworks for model usage access control and operational oversight.

Technical Leadership & Collaboration

Act as technical thought leader for AI initiatives across delivery teams.

Collaborate with product owners UX designers data engineers cloud engineers and DevOps teams.

Provide architecture guidance reviews and mentorship to senior engineers.

Communicate complex AI concepts clearly to technical and non technical stakeholders.

AI / GenAI Strong experience with Generative AI and Agentic AI architectures Hands on knowledge of LLMs embeddings RAG pipelines prompt engineering and agent frameworks Proficiency in Python for AI/ML development ML & Data Engineering Experience with ML/DL frameworks such as TensorFl...