Job Title
- AI Architect Location
- Work At Home
- MA, Work At Home, Massachusetts, United States of America, 01604
Duration:
3 months Temporary to Permanent position for the right candidate. This role is remote
- required to work Eastern hours The AI Architect is responsible for designing, developing, and governing enterprise-grade AI solutions that align with business strategy.
This role blends deep technical expertise in artificial intelligence, machine learning, data, and cloud architecture with strong product intuition, security awareness, and leadership. The AI Architect ensures that AI initiatives are scalable, ethical, secure, cost-efficient, and integrated into the broader enterprise ecosystem. Key Responsibilities AI Strategy & Solution Architecture Define and evolve the enterprise AI architecture, ensuring alignment with business, data, and technology strategies. Design scalable, secure, and compliant automation solutions to streamline processes across the enterprise. Architect end-to-end AI solutions including data engineering, RAG model development, model operations (MLOps), and lifecycle management. Partner with business, product, and engineering teams to translate business problems into appropriate AI/ML approaches. Develop reference architectures and reusable patterns for generative AI, Agentic AI, predictive models, conversational systems, and intelligent automation. Technical Leadership Provide architectural oversight across AI/ML projects to ensure consistency, performance, and maintainability. Evaluate and select AI technologies, frameworks, cloud services, vector databases, LLM orchestration frameworks, and tooling. Support development teams on model selection, training pipelines, prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and evaluation methodologies. Mentor engineers, analysts, and product teams on AI best practices. Data, Integration & Platforms Partner with data architects and engineering to ensure robust data pipelines, governance, feature stores, and architecture. Design secure and performant integration between AI models and enterprise systems (APIs, microservices, events). Governance & Compliance Ensure AI solutions adhere to enterprise security standards, data privacy policies, and regulatory requirements. Implement responsible AI guardrails, fairness checks, explainability frameworks, and monitoring. Develop and maintain automation governance frameworks, documentation, and audit trails. Operations & Optimization Define MLOps / LLMOps standards including CI/CD pipelines, model monitoring, drift detection, observability, and rollback processes. Drive continuous improvement of model performance, cost optimization, and operational efficiency. Establish KPIs, telemetry, and feedback loops for production AI systems. Collaboration & Enablement Partner with IT, compliance, operations, and customer service teams to align automation initiatives with business goals. Mentor and guide developers and analysts to build a center of excellence (CoE) for automation. Required Qualifications Bachelor's degree in Computer Science, Engineering, or a related technical field. 5 years of experience in application development, engineering, or solution delivery roles. 1 year of hands-on experience in AI/ML engineering, data science, or AI solution architecture. Strong hands-on experience with machine learning frameworks and LLM platforms (e.g., OpenAI, Azure AI Foundry, Copilot Studio/Agent Builder, or comparable generative AI ecosystems). Deep expertise in cloud platforms, particularly Microsoft Azure, and modern architectural patterns (microservices, event-driven architectures, API-first design). Proficiency in one or more of the following: Python, Azure Machine Learning, or related AI/ML tooling. Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores. Strong hands-on experience with ML frameworks, LLM platforms
- OpenAI, MSFT/Azure Cloud foundry, Copilot Studio Agent builder, low code/no code platforms, and generative AI tools.
Background in RAG systems, model fine-tuning, embeddings, vector storage, and retrieval optimization. Preferred Qualifications Experience in enterprise-wide AI programs or platform buildouts. Strong understanding of data governance, privacy, security, and model risk management. Prior experience with large-scale transformation programs.
