Note: The job is a remote job and is open to candidates in USA. NewRocket is seeking a Senior Forward Deployed AI Engineer to help banking, financial services, and fintech customers deploy, operationalize, and scale AI-powered workflow solutions. The role combines full-stack engineering, enterprise integration, generative AI implementation, responsible AI evaluation, and client-facing delivery across ServiceNow and broader enterprise ecosystems.
Responsibilities
- Deploy, configure, and operationalize agentic AI workflows, AI assistants, and AI-powered automations within client ServiceNow environments and enterprise technology ecosystems
- Translate customer business requirements, operational processes, and desired outcomes into technical architectures, implementation plans, and production-ready AI solutions
- Implement and integrate NewRocket Agent Packs, AI accelerators, and workflow solutions into enterprise environments
- Work directly with customer teams to tailor AI solutions to their operating models, business processes, data sources, security requirements, and user needs
- Support workshops, discovery sessions, technical working sessions, demonstrations, pilots, and production rollouts
- Clearly communicate AI capabilities, limitations, tradeoffs, solution behavior, and adoption considerations to technical and business stakeholders
- Build enterprise AI applications and workflows using Claude, the Anthropic API, and other LLM platforms as appropriate for the client use case
- Apply effective prompt and context-engineering techniques, including clear instructions, examples, role and task definition, structured inputs, response constraints, and long-context management
- Design and implement AI workflows using structured outputs, tool use/function calling, API integrations, multi-step orchestration, and human-in-the-loop review patterns
- Build retrieval-augmented generation (RAG) solutions that ground AI responses in authorized enterprise data and knowledge sources
- Implement practical techniques to improve reliability and user trust, including citation or source-grounding patterns, validation, confidence thresholds, output schemas, fallback handling, and escalation workflows
- Stay current on Anthropic platform capabilities, Claude model releases, implementation guidance, responsible AI principles, and enterprise deployment best practices
- Complete relevant Anthropic training, partner enablement, and technical education programs as available, and incorporate those practices into NewRocket solution delivery
- Build demos, prototypes, and proof-of-concept implementations that validate AI-driven workflows and customer use cases
- Rapidly iterate with customers and internal teams to refine AI-powered solutions based on feedback, performance results, and operational needs
- Support the design and implementation of AI orchestration, LLM integrations, agentic decision models, and workflow automation patterns
- Evaluate when an agentic approach is appropriate versus deterministic automation, traditional workflow logic, search, analytics, or human review
- Develop reusable implementation patterns, solution templates, prompt libraries, integrations, and deployment assets that accelerate future client delivery
- Develop secure integrations between ServiceNow, enterprise systems, APIs, data platforms, and AI services
- Build supporting components such as scripts, microservices, automation logic, integration services, and lightweight user interfaces
- Implement integrations with AI platforms, enterprise APIs, identity systems, document repositories, databases, and structured and unstructured data sources
- Apply sound engineering practices for authentication, authorization, secrets management, access controls, logging, error handling, version control, and documentation
- Design solutions that meet enterprise expectations for security, scalability, maintainability, observability, and production readiness
- Develop and execute practical evaluation approaches for AI applications, including test cases, representative datasets, success metrics, and regression testing
- Assess AI workflow quality across dimensions such as relevance, accuracy, groundedness, task completion, safety, latency, cost, and user experience
- Implement safeguards for sensitive data, role-based permissions, appropriate data access, prompt injection risks, unsafe tool use, and unintended model behavior
- Establish human-in-the-loop workflows for sensitive, high-impact, low-confidence, or exception-based decisions
- Document AI solution behavior, known limitations, risk controls, governance considerations, and operational support procedures
- Monitor and improve deployed solutions based on user feedback, usage patterns, performance data, incidents, and evolving customer needs
- Present and demo solutions to key client stakeholders
- Engage in pre-sales activities as needed
- Identifying common patterns, requirements, integration needs, and capabilities discovered through customer deployments
- Contributing reusable assets, integration components, prompt patterns, evaluation frameworks, and automation capabilities
- Providing actionable product feedback that improves usability, reliability, scalability, security, and customer value
- Helping transform successful client implementations into repeatable platform features, accelerators, and delivery playbooks
- Supporting the definition of standards and best practices for enterprise AI delivery across NewRocket’s AI Foundry
- Diagnose and resolve technical issues across AI workflows, integrations, retrieval pipelines, data connections, and automation processes
- Troubleshoot issues related to model inputs and outputs, prompt behavior, tool execution, API reliability, permissions, data quality, and system performance
- Ensure deployed AI solutions are secure, scalable, supportable, and production-ready
- Optimize deployed systems for reliability, performance, latency, model usage, and cost efficiency
- Work closely with Business Process Consultants, Product Engineering, Data Engineers, AI/ML Engineers, ServiceNow teams, and the AI Center of Excellence
- Serve as the engineering counterpart to consulting and delivery teams throughout discovery, solution design, implementation, rollout, and continuous improvement
- Contribute to internal playbooks, technical documentation, reusable deployment patterns, reference architectures, and product evolution
- Share lessons learned from customer deployments to strengthen NewRocket’s AI delivery capabilities and solution portfolio
Skills
- * **8+ years** of experience in software engineering, systems integration, enterprise platforms, workflow automation, or related technical delivery roles
- * **Deep understanding of, and experience with banking systems and workflows.**
- * Strong engineering foundation, with hands-on experience in full-stack development, scripting, APIs, microservices, enterprise integrations, or cloud-native applications
- * Experience building, deploying, or supporting AI/LLM-powered applications, AI-enabled automations, conversational experiences, RAG systems, or agentic workflows
- * Experience integrating APIs, enterprise applications, data platforms, or workflow systems in production environments
- * Proficiency in **JavaScript/TypeScript**, **Python**, or similar programming and scripting languages
- * Experience with cloud platforms such as **AWS, Microsoft Azure, or Google Cloud**
- * Familiarity with modern LLM application concepts, including prompt engineering, context windows, token usage, embeddings, vector search, RAG, tool use/function calling, structured outputs, and model evaluation
- * Understanding of responsible AI concepts, including hallucination mitigation, sensitive-data handling, identity and access controls, human oversight, AI safety, and secure AI deployment
- * Experience operating in customer-facing engineering, consulting, technical implementation, solutions architecture, or professional-services roles
- * Strong problem-solving skills and the ability to communicate complex technical concepts clearly to both technical and business stakeholders
- * Ability to manage ambiguity, prioritize effectively, travel approximately 25%, and deliver high-quality solutions in fast-moving customer environments
- * Hands-on experience with the **Anthropic API**, **Claude models**, Anthropic Console, Claude Code, or Anthropic implementation guidance
- * Completion of relevant Anthropic Academy learning, partner enablement, technical training, or equivalent hands-on experience deploying Claude-based solutions
- * Experience applying Claude capabilities such as long-context processing, tool use, structured outputs, document analysis, and enterprise knowledge workflows
- * Familiarity with Model Context Protocol (**MCP**) concepts and experience building or integrating secure tools and data connections for AI applications
- * Experience with ServiceNow development, configuration, workflow automation, or enterprise platform implementation
- * Familiarity with ServiceNow scripting, APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, and platform development patterns
- * Understanding of ServiceNow data models and enterprise workflow concepts, including: + CMDB + ITSM, CSM, HRSD, or employee workflows + Workflow and task tables + Knowledge management + Enterprise integrations and data exchange patterns
- * Experience building AI prototypes, RAG systems, semantic search capabilities, AI assistants, or agent-based workflows
- * Experience integrating LLM APIs or AI services into enterprise applications and business processes
- * Experience with vector databases, embeddings, document ingestion, chunking, retrieval strategies, and knowledge-grounding patterns
- * Familiarity with LLM orchestration and application frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or comparable tools
- * Experience with AI observability, tracing, evaluation frameworks, prompt/version management, monitoring, and cost optimization
- * Experience contributing to internal platforms, reusable accelerators, developer tooling, or product capabilities
- * Familiarity with Docker, CI/CD, SQL, Git, infrastructure-as-code, and secure cloud deployment practices
Benefits
- Remote work with travel (~60%)
Company Overview