Agentic AI Architect-Anthropic PartnershipThe RoleNewRocket is hiring an experienced Agentic AI Architect to support a large global client and help shape, design, and deploy enterprise AI solutions.
This role combines AI/ML architecture, generative AI engineering, data engineering, cloud solution design, and technical leadership. You will be responsible for defining scalable architectures for AI and machine learning applications, including predictive analytics, natural language processing, retrieval-augmented generation (RAG), intelligent automation, and agentic AI systems.
The ideal candidate brings strong hands-on expertise in Python, SQL, cloud services, databases, big-data technologies, and modern machine learning frameworks. You will also have practical experience designing LLM-powered applications that safely connect models to enterprise knowledge, systems, APIs, and workflows.
You will work closely with client stakeholders, NewRocket consultants, ServiceNow teams, data engineers, and AI/ML engineers to translate business needs into secure, reliable, and production-ready AI solutions.
What You Will Be DoingAI/ML Architecture & Solution Delivery
- Architect, develop, deploy, and maintain scalable AI and machine learning solutions for enterprise use cases.
- Define end-to-end solution architectures spanning data ingestion, data preparation, model selection, orchestration, APIs, workflow integrations, user experiences, monitoring, and governance.
- Partner with business and technical stakeholders to identify high-value AI opportunities and translate requirements into actionable technical designs and delivery roadmaps.
- Design solutions for predictive analytics, classification, clustering, forecasting, anomaly detection, recommendation, and intelligent automation.
- Establish technical standards, reference architectures, reusable patterns, and best practices for enterprise AI delivery.
- Lead technical discovery, architecture workshops, design reviews, proof-of-concepts, and client demonstrations.
Generative AI, Anthropic & LLM Engineering- Design and implement enterprise generative AI applications using Claude, the Anthropic API, and other LLM platforms when appropriate for the business use case.
- Apply effective prompt and context-engineering practices, including instruction design, few-shot examples, role definition, structured inputs and outputs, response constraints, and long-context management.
- Architect retrieval-augmented generation (RAG) solutions that securely ground model outputs in approved enterprise documents, knowledge bases, databases, and other data sources.
- Design document ingestion, chunking, embedding, vector search, retrieval, reranking, citation, and response-generation patterns for enterprise knowledge workflows.
- Build and integrate AI capabilities using structured outputs, tool use/function calling, APIs, and workflow orchestration.
- Assess and recommend the appropriate balance of LLMs, traditional machine learning, deterministic automation, enterprise search, and human decision-making for each use case.
- Stay current on Anthropic platform capabilities, Claude releases, Anthropic implementation guidance, responsible AI principles, and enterprise AI best practices.
- Complete relevant Anthropic partner enablement, technical training, and product education as available through NewRocket's partnership.
- Travel to clients and conferences as needed.
Agentic AI & Workflow Orchestration- Architect and implement agentic AI systems that can reason over enterprise context, use authorized tools, execute multi-step tasks, and coordinate work across enterprise applications.
- Design AI agents with defined roles, task boundaries, tool permissions, memory and context strategies, approval gates, fallback paths, and escalation mechanisms.
- Develop agentic workflows that integrate with ServiceNow, enterprise APIs, cloud services, databases, collaboration platforms, and operational systems.
- Implement human-in-the-loop controls for sensitive, high-impact, low-confidence, or exception-based actions.
- Design safeguards to prevent unintended tool execution, unauthorized data access, prompt injection, unsafe outputs, and uncontrolled autonomous behavior.
- Evaluate agent effectiveness through task-completion rates, quality, reliability, latency, cost, safety, and user-adoption measures.
Machine Learning, NLP & Data Science- Develop, train, validate, deploy, and monitor machine learning models for predictive analytics, classification, clustering, and related use cases.
- Implement AI and ML solutions using frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Develop NLP capabilities using Hugging Face transformer models and cloud AI services for text classification, summarization, sentiment analysis, entity extraction, and document intelligence.
- Evaluate, adapt, and where appropriate fine-tune open-source or client-approved models for targeted use cases; apply prompt engineering, RAG, and tool use as preferred strategies when model fine-tuning is not appropriate or available.
- Work with large language models for conversational AI, text generation, summarization, knowledge assistance, workflow automation, and decision support.
- Build data-processing and feature-engineering pipelines that support reliable model training, testing, deployment, and monitoring.
Cloud, Data Engineering & Platform Integration- Design and implement AI solutions using cloud platforms including AWS (e.g., SageMaker, Lambda, S3), Microsoft Azure, and Google Cloud Platform (e.g., Vertex AI).
- Develop integrations between AI services, ServiceNow, enterprise applications, APIs, identity providers, databases, document repositories, and data platforms.
- Build supporting services, APIs, microservices, automation logic, and integration components required to operationalize AI solutions.
- Work with big-data technologies such as Apache Spark and Snowflake for large-scale data processing, analytics, and AI data pipelines.
- Design and optimize ETL/ELT pipelines for data ingestion, transformation, validation, quality management, governance, and observability.
- Use SQL, MySQL, PostgreSQL, MongoDB, and comparable technologies for data modeling, database management, query optimization, and data warehousing.
- Apply secure engineering practices for authentication, authorization, secrets management, encryption, logging, error handling, and access controls.
AI Quality, Governance & Responsible AI- Establish evaluation frameworks, test suites, representative datasets, and regression-testing practices for AI, ML, and agentic solutions.
- Measure and optimize solution quality across relevance, accuracy, groundedness, safety, task completion, model behavior, latency, cost, reliability, and user experience.
- Implement observability, monitoring, tracing, alerting, and feedback loops for production AI applications.
- Define practical governance approaches for data privacy, sensitive-data handling, model access, auditability, model limitations, and responsible AI usage.
- Implement controls for role-based access, data permissions, grounded responses, source attribution, output validation, exception handling, and human review.
- Document architecture decisions, AI-system behavior, limitations, risk controls, operating procedures, and support requirements.
ServiceNow, Automation & Enterprise Experience- Design AI-enabled workflow automations and intelligent experiences within ServiceNow and connected enterprise ecosystems.
- Support chatbot, virtual-agent, employee-support, customer-service, IT operations, knowledge-management, and workflow-automation use cases.
- Integrate AI solutions with ServiceNow capabilities such as workflow automation, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, knowledge management, and enterprise data sources, where applicable.
- Develop AI automation solutions using collaboration platforms and cloud AI services, including Microsoft Teams and Azure AI, where appropriate.
- Contribute reusable implementation patterns, agent designs, integration components, evaluation assets, and accelerators that strengthen NewRocket's AI Foundry capabilities.
Analytics & Visualization- Create interactive data visualizations and executive-ready reporting using tools such as Tableau and Power BI.
- Develop dashboards and measurement frameworks that help stakeholders understand AI adoption, workflow outcomes, business value, operational performance, and model quality.
- Support the definition and tracking of KPIs that demonstrate measurable value from AI-enabled solutions.
What You Bring Along- 10+ years of experience applying AI, machine learning, data science, software engineering, or intelligent automation technologies to practical enterprise use cases.
- Strong coding expertise in Python and SQL, with hands-on experience using ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Demonstrated experience architecting or delivering AI/ML, LLM, RAG, conversational AI, agentic AI, or AI-powered automation solutions.
- Strong understanding of algorithms, object-oriented programming, functional design principles, software architecture, and API-based integration patterns.
- Hands-on experience with LLM concepts and application-development patterns, including prompt engineering, context management, tokens, embeddings, vector search, RAG, tool use, structured outputs, and model evaluation.
- Proficiency with data-science libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
- Hands-on experience with cloud platforms such as AWS, Azure, and/or Google Cloud.
- Experience with big-data processing and cloud data platforms, including Apache Spark and Snowflake.
- NLP experience using Hugging Face, transformer models, cloud AI services, or comparable technologies.
- Strong understanding of database management, query optimization, data warehousing, ETL/ELT pipelines, and data-quality practices.
- Experience with data visualization tools such as Tableau and Power BI.
- Knowledge of responsible AI practices, including privacy, security, human oversight, hallucination mitigation, prompt-injection defenses, model limitations, and governed deployment.
- Strong analytical, architectural, problem-solving, communication, and stakeholder-management skills.
- Ability to collaborate effectively across client, consulting, engineering, product, data, and AI/ML teams.
- Ability to learn and adapt quickly as AI technologies, enterprise requirements, and client needs evolve.
Preferred QualificationsAnthropic & Claude Experience- Hands-on experience with Claude, the Anthropic API, Anthropic Console, Claude Code, or Anthropic-focused implementation guidance.
- Completion of Anthropic Academy courses, partner enablement, technical training, or equivalent experience building Claude-powered enterprise applications.
- Experience using Claude capabilities such as long-context processing, document analysis, tool use, structured outputs, and enterprise knowledge workflows.
- Familiarity with Model Context Protocol (MCP) concepts and secure patterns for connecting AI applications to enterprise tools and data sources.
Agentic AI & LLM Operations- Experience designing AI agents, multi-agent systems, AI orchestration workflows, and human-in-the-loop operating models.
- Experience with LLM application frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or comparable frameworks.
- Experience with vector databases, semantic retrieval, enterprise search, embeddings, document ingestion, reranking, and RAG evaluation.
- Familiarity with AI observability, tracing, prompt/version management, evaluation frameworks, guardrails, model monitoring, and cost optimization.
- Experience with MLOps, LLMOps, CI/CD, Docker, Kubernetes, infrastructure as code, and production cloud deployment practices.
Education- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence/Machine Learning, Engineering, or a related technical field; equivalent relevant experience will also be considered.
- Advanced degree or relevant AI, cloud, data, ServiceNow, or Anthropic certifications are a plus.
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