Hollstadt Consulting
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AI/ML Architect
role at
Hollstadt Consulting
This range is provided by Hollstadt Consulting. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base Pay Range $135,000.00 / yr – $175,000.00 / yr
Role AI/ML Architect
Location Remote
Salary $135,000 – $175,000, dependent on skills and qualifications
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Business Consulting and Services
Position Summary The AI/ML Architect will play a critical, hands‑on leadership role within the AI Center of Excellence (CoE), shaping the architecture and strategic direction of enterprise‑grade AI solutions. This position exists to design, build, and scale both traditional ML and Generative AI (GenAI) solutions that are robust, production‑ready, and aligned with business objectives. The ideal candidate will have deep technical expertise and a strong execution mindset, someone who can drive solutions from idea to implementation.
This role works across a diverse ecosystem including AWS, Salesforce, Snowflake, and Oracle to support prioritized enterprise initiatives.
Accountabilities Engineering and Architecture
Lead the hands‑on architecture, development, and deployment of production‑grade AI/ML systems, ensuring scalability, reliability, performance, and cost‑efficiency.
Architect traditional ML solutions (e.g., classification, regression, recommendation systems) and advanced GenAI systems including Retrieval‑Augmented Generation (RAG) and Agentic AI.
Design and implement cloud‑native AI/ML pipelines using cloud platforms.
Evaluate, prototype, and build PoCs regularly to test architectural decisions, validate feasibility, and accelerate solution delivery.
Integrate and deploy multiple LLMs (e.g., from OpenAI, Claude, Gemini, LLaMA, Hugging Face) and vector databases (e.g., Pinecone, Qdrant, pgvector, Milvus, Weaviate).
Create reusable frameworks and solution templates that drive consistency, speed, and quality across AI initiatives.
Ensure all solutions are aligned with responsible AI standards, security best practices, and enterprise governance.
Design, Innovation & Strategy
Explore and implement emerging AI paradigms, including agentic AI, Model Context Protocol (MCP), and Google’s Agent‑to‑Agent (A2A) protocol.
Drive innovation by recommending and evaluating GenAI tools, third‑party orchestration frameworks, and SaaS integrations.
Develop a forward‑looking technical roadmap that balances short‑term deliverables with long‑term innovation.
Stay current with cutting‑edge trends in LLMOps, AI observability, and intelligent automation platforms.
Leverage AI coding assistants (e.g., Claude Code, AWS Bedrock, GitHub Copilot) to boost productivity.
Collaboration and Enablement
Collaborate with data scientists, ML engineers, software engineers, and enterprise architects to translate business needs into scalable AI solutions.
Provide technical guidance, mentorship, and architectural direction to teams working across the AI/ML lifecycle.
Work in agile teams, contributing hands‑on while also shaping backlog priorities and solution design.
Partner cross‑functionally to integrate AI into enterprise systems (e.g., Salesforce, Snowflake, Oracle).
Required Qualifications Knowledge of:
Scalable architecture patterns for traditional ML and GenAI.
Multi‑cloud AI/ML services including AWS (SageMaker, Bedrock etc) and at least one of Azure (ML, OpenAI) or GCP (Vertex AI).
Strong familiarity with multiple LLMs and embedding models (e.g., OpenAI, Anthropic, Meta, Google, Hugging Face).
Proficiency in contextual memory and multiple vector databases for semantic search.
MLOps and LLMOps practices, including CI/CD, model monitoring, versioning, drift detection, and governance.
Prompt engineering and management practices, including prompt versioning, A/B testing of prompts, and experience with prompt management tools.
AI/ML observability stacks such as Weights & Biases, Langsmith or similar tools.
Required Skills and Abilities:
Hands‑on experience designing and building AI/ML solutions from prototype to production.
Strong Python development skills, including frameworks and libraries for ML, GenAI, and software engineering best practices.
Experience with TensorFlow and/or PyTorch for training and deploying models.
Deep understanding of software engineering, including modular design, testing, version control (Git), and CI/CD pipelines.
Proven track record of building and running PoCs to validate architecture and feasibility.
Experience working in agile environments, participating in sprints and cross‑functional delivery.
Ability to communicate technical concepts clearly to a wide range of stakeholders.
Eagerness and ability to quickly learn and apply new AI/ML and automation technologies.
Education and Experience
Bachelor’s degree in computer science, engineering, or a related field (Master’s preferred).
8+ years of experience in AI/ML engineering or architecture roles.
Strong portfolio of real‑world deployments in both traditional ML and GenAI.
Preferred Qualifications
Master’s or PhD in a technical field.
Experience architecting agentic AI systems and multi‑agent orchestration workflows.
Experience in regulated industries, especially healthcare or finance.
AI/ML certifications from AWS, Azure, or GCP.
Contributions to open‑source AI/ML projects or published research.
Referrals increase your chances of interviewing at Hollstadt Consulting by 2×.
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AI/ML Architect
role at
Hollstadt Consulting
This range is provided by Hollstadt Consulting. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base Pay Range $135,000.00 / yr – $175,000.00 / yr
Role AI/ML Architect
Location Remote
Salary $135,000 – $175,000, dependent on skills and qualifications
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Business Consulting and Services
Position Summary The AI/ML Architect will play a critical, hands‑on leadership role within the AI Center of Excellence (CoE), shaping the architecture and strategic direction of enterprise‑grade AI solutions. This position exists to design, build, and scale both traditional ML and Generative AI (GenAI) solutions that are robust, production‑ready, and aligned with business objectives. The ideal candidate will have deep technical expertise and a strong execution mindset, someone who can drive solutions from idea to implementation.
This role works across a diverse ecosystem including AWS, Salesforce, Snowflake, and Oracle to support prioritized enterprise initiatives.
Accountabilities Engineering and Architecture
Lead the hands‑on architecture, development, and deployment of production‑grade AI/ML systems, ensuring scalability, reliability, performance, and cost‑efficiency.
Architect traditional ML solutions (e.g., classification, regression, recommendation systems) and advanced GenAI systems including Retrieval‑Augmented Generation (RAG) and Agentic AI.
Design and implement cloud‑native AI/ML pipelines using cloud platforms.
Evaluate, prototype, and build PoCs regularly to test architectural decisions, validate feasibility, and accelerate solution delivery.
Integrate and deploy multiple LLMs (e.g., from OpenAI, Claude, Gemini, LLaMA, Hugging Face) and vector databases (e.g., Pinecone, Qdrant, pgvector, Milvus, Weaviate).
Create reusable frameworks and solution templates that drive consistency, speed, and quality across AI initiatives.
Ensure all solutions are aligned with responsible AI standards, security best practices, and enterprise governance.
Design, Innovation & Strategy
Explore and implement emerging AI paradigms, including agentic AI, Model Context Protocol (MCP), and Google’s Agent‑to‑Agent (A2A) protocol.
Drive innovation by recommending and evaluating GenAI tools, third‑party orchestration frameworks, and SaaS integrations.
Develop a forward‑looking technical roadmap that balances short‑term deliverables with long‑term innovation.
Stay current with cutting‑edge trends in LLMOps, AI observability, and intelligent automation platforms.
Leverage AI coding assistants (e.g., Claude Code, AWS Bedrock, GitHub Copilot) to boost productivity.
Collaboration and Enablement
Collaborate with data scientists, ML engineers, software engineers, and enterprise architects to translate business needs into scalable AI solutions.
Provide technical guidance, mentorship, and architectural direction to teams working across the AI/ML lifecycle.
Work in agile teams, contributing hands‑on while also shaping backlog priorities and solution design.
Partner cross‑functionally to integrate AI into enterprise systems (e.g., Salesforce, Snowflake, Oracle).
Required Qualifications Knowledge of:
Scalable architecture patterns for traditional ML and GenAI.
Multi‑cloud AI/ML services including AWS (SageMaker, Bedrock etc) and at least one of Azure (ML, OpenAI) or GCP (Vertex AI).
Strong familiarity with multiple LLMs and embedding models (e.g., OpenAI, Anthropic, Meta, Google, Hugging Face).
Proficiency in contextual memory and multiple vector databases for semantic search.
MLOps and LLMOps practices, including CI/CD, model monitoring, versioning, drift detection, and governance.
Prompt engineering and management practices, including prompt versioning, A/B testing of prompts, and experience with prompt management tools.
AI/ML observability stacks such as Weights & Biases, Langsmith or similar tools.
Required Skills and Abilities:
Hands‑on experience designing and building AI/ML solutions from prototype to production.
Strong Python development skills, including frameworks and libraries for ML, GenAI, and software engineering best practices.
Experience with TensorFlow and/or PyTorch for training and deploying models.
Deep understanding of software engineering, including modular design, testing, version control (Git), and CI/CD pipelines.
Proven track record of building and running PoCs to validate architecture and feasibility.
Experience working in agile environments, participating in sprints and cross‑functional delivery.
Ability to communicate technical concepts clearly to a wide range of stakeholders.
Eagerness and ability to quickly learn and apply new AI/ML and automation technologies.
Education and Experience
Bachelor’s degree in computer science, engineering, or a related field (Master’s preferred).
8+ years of experience in AI/ML engineering or architecture roles.
Strong portfolio of real‑world deployments in both traditional ML and GenAI.
Preferred Qualifications
Master’s or PhD in a technical field.
Experience architecting agentic AI systems and multi‑agent orchestration workflows.
Experience in regulated industries, especially healthcare or finance.
AI/ML certifications from AWS, Azure, or GCP.
Contributions to open‑source AI/ML projects or published research.
Referrals increase your chances of interviewing at Hollstadt Consulting by 2×.
#J-18808-Ljbffr