{"id":1200038,"url":"https://alion.io/job/newrocket-senior-forward-deployed-ai-engineer-bankingfinancial-services","title":"Senior Forward Deployed AI Engineer-Banking/Financial Services","company":{"id":4730,"name":"NewRocket","domain":"newrocket.com","url":"https://alion.io/company/newrocket","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":25,"computed_at":"2026-09-25T05:45:01Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":140000,"max_usd":255000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":637},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"ServiceNow","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"CI/CD","optional":true},{"name":"Claude Code","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"ITSM","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-09-24T19:04:39Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-09-26T00:08:16Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Senior Forward Deployed AI Engineer-Banking/Financial Services\nAI Foundry | NewRocket\nLocation: Remote with travel (~60%)\nRole Overview\nNewRocket is seeking a highly skilled Senior Forward Deployed AI Engineer to join the AI Foundry team and work directly with banking, financial services and fintech customers to deploy, operationalize, and scale AI-powered workflow solutions.\nThis role blends full-stack engineering, enterprise integration, generative AI implementation, and client-facing solution delivery. Forward Deployed AI Engineers partner closely with business consultants, product teams, AI/ML engineers, and customer stakeholders to translate real-world business problems into secure, reliable, deployable AI-driven solutions.\nAs an Anthropic partner/vendor, NewRocket is expanding its capability to design and deliver enterprise solutions using Claude and other leading AI technologies. In this role, you will apply modern LLM engineering practices-including prompt and context engineering, retrieval-augmented generation (RAG), tool use, structured outputs, agentic workflows, model evaluation, and responsible AI controls-to deliver measurable customer value.\nYou will help customers implement agentic AI workflows, intelligent automations, and AI-powered integrations within ServiceNow and broader enterprise ecosystems. You will also contribute directly to the evolution of NewRocket’s AI platforms, accelerators, and intellectual property, including the NewRocket Intelligence Platform, Value Realization Dashboard, Data Intelligence Platform, and reusable Agent Packs.\nThis role requires strong engineering skills, curiosity about emerging AI technologies, sound judgment regarding responsible AI deployment, and the ability to operate effectively in fast-moving customer environments.\nKey Responsibilities\nClient Delivery & AI Solution Implementation\nDeploy, configure, and operationalize agentic AI workflows, AI assistants, and AI-powered automations within client ServiceNow environments and enterprise technology ecosystems.\nTranslate customer business requirements, operational processes, and desired outcomes into technical architectures, implementation plans, and production-ready AI solutions.\nImplement and integrate NewRocket Agent Packs, AI accelerators, and workflow solutions into enterprise environments.\nWork directly with customer teams to tailor AI solutions to their operating models, business processes, data sources, security requirements, and user needs.\nSupport workshops, discovery sessions, technical working sessions, demonstrations, pilots, and production rollouts.\nClearly communicate AI capabilities, limitations, tradeoffs, solution behavior, and adoption considerations to technical and business stakeholders.\nAnthropic and Generative AI Engineering\nBuild enterprise AI applications and workflows using Claude, the Anthropic API, and other LLM platforms as appropriate for the client use case.\nApply effective prompt and context-engineering techniques, including clear instructions, examples, role and task definition, structured inputs, response constraints, and long-context management.\nDesign and implement AI workflows using structured outputs, tool use/function calling, API integrations, multi-step orchestration, and human-in-the-loop review patterns.\nBuild retrieval-augmented generation (RAG) solutions that ground AI responses in authorized enterprise data and knowledge sources.\nImplement practical techniques to improve reliability and user trust, including citation or source-grounding patterns, validation, confidence thresholds, output schemas, fallback handling, and escalation workflows.\nStay current on Anthropic platform capabilities, Claude model releases, implementation guidance, responsible AI principles, and enterprise deployment best practices.\nComplete relevant Anthropic training, partner enablement, and technical education programs as available, and incorporate those practices into NewRocket solution delivery.\nSolution Engineering & Prototyping\nBuild demos, prototypes, and proof-of-concept implementations that validate AI-driven workflows and customer use cases.\nRapidly iterate with customers and internal teams to refine AI-powered solutions based on feedback, performance results, and operational needs.\nSupport the design and implementation of AI orchestration, LLM integrations, agentic decision models, and workflow automation patterns.\nEvaluate when an agentic approach is appropriate versus deterministic automation, traditional workflow logic, search, analytics, or human review.\nDevelop reusable implementation patterns, solution templates, prompt libraries, integrations, and deployment assets that accelerate future client delivery.\nFull-Stack Engineering & Integration\nDevelop secure integrations between ServiceNow, enterprise systems, APIs, data platforms, and AI services.\nBuild supporting components such as scripts, microservices, automation logic, integration services, and lightweight user interfaces.\nImplement integrations with AI platforms, enterprise APIs, identity systems, document repositories, databases, and structured and unstructured data sources.\nApply sound engineering practices for authentication, authorization, secrets management, access controls, logging, error handling, version control, and documentation.\nDesign solutions that meet enterprise expectations for security, scalability, maintainability, observability, and production readiness.\nAI Quality, Evaluation & Responsible AI\nDevelop and execute practical evaluation approaches for AI applications, including test cases, representative datasets, success metrics, and regression testing.\nAssess AI workflow quality across dimensions such as relevance, accuracy, groundedness, task completion, safety, latency, cost, and user experience.\nImplement safeguards for sensitive data, role-based permissions, appropriate data access, prompt injection risks, unsafe tool use, and unintended model behavior.\nEstablish human-in-the-loop workflows for sensitive, high-impact, low-confidence, or exception-based decisions.\nDocument AI solution behavior, known limitations, risk controls, governance considerations, and operational support procedures.\nMonitor and improve deployed solutions based on user feedback, usage patterns, performance data, incidents, and evolving customer needs.\nProduct & Platform Contribution\nActively contribute to the development and evolution of NewRocket’s AI intellectual property and platforms, including:\nNewRocket Intelligence Platform\nValue Realization Dashboard\nData Intelligence Platform\nAgent Packs and reusable AI solution accelerators\nResponsibilities include:\nPresent and demo solutions to key client stakeholders\nEngage in pre-sales activities as needed\nIdentifying common patterns, requirements, integration needs, and capabilities discovered through customer deployments.\nContributing reusable assets, integration components, prompt patterns, evaluation frameworks, and automation capabilities.\nProviding actionable product feedback that improves usability, reliability, scalability, security, and customer value.\nHelping transform successful client implementations into repeatable platform features, accelerators, and delivery playbooks.\nSupporting the definition of standards and best practices for enterprise AI delivery across NewRocket’s AI Foundry.\nSystems Integration & Troubleshooting\nDiagnose and resolve technical issues across AI workflows, integrations, retrieval pipelines, data connections, and automation processes.\nTroubleshoot issues related to model inputs and outputs, prompt behavior, tool execution, API reliability, permissions, data quality, and system performance.\nEnsure deployed AI solutions are secure, scalable, supportable, and production-ready.\nOptimize deployed systems for reliability, performance, latency, model usage, and cost efficiency.\nCross-Team Collaboration\nWork closely with Business Process Consultants, Product Engineering, Data Engineers, AI/ML Engineers, ServiceNow teams, and the AI Center of Excellence.\nServe as the engineering counterpart to consulting and delivery teams throughout discovery, solution design, implementation, rollout, and continuous improvement.\nContribute to internal playbooks, technical documentation, reusable deployment patterns, reference architectures, and product evolution.\nShare lessons learned from customer deployments to strengthen NewRocket’s AI delivery capabilities and solution portfolio.\nWhat Success Looks Like in the First 6 Months\nSuccessfully deploy AI-powered workflows, assistants, automations, or integrations across multiple customer engagements.\nDeliver secure, reliable AI workflows within customer ServiceNow environments and connected enterprise systems.\nBuild trusted relationships with customer technical teams, business stakeholders, and NewRocket delivery teams.\nDemonstrate strong practical application of Claude and modern LLM engineering practices, including prompt/context engineering, tool use, RAG, evaluations, and responsible AI controls.\nContribute reusable components, implementation patterns, prompt assets, and improvements to NewRocket’s AI platforms and accelerators.\nProvide actionable customer-driven feedback that improves the NewRocket Intelligence Platform and related products.\nHelp establish repeatable methods for moving AI use cases from prototype through governed production deployment.\nRequired Qualifications\n8+ years of experience in software engineering, systems integration, enterprise platforms, workflow automation, or related technical delivery roles.\nDeep understanding of, and experience with banking systems and workflows.\nStrong engineering foundation, with hands-on experience in full-stack development, scripting, APIs, microservices, enterprise integrations, or cloud-native applications.\nExperience building, deploying, or supporting AI/LLM-powered applications, AI-enabled automations, conversational experiences, RAG systems, or agentic workflows.\nExperience integrating APIs, enterprise applications, data platforms, or workflow systems in production environments.\nProficiency in JavaScript/TypeScript, Python, or similar programming and scripting languages.\nExperience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.\nFamiliarity 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.\nUnderstanding of responsible AI concepts, including hallucination mitigation, sensitive-data handling, identity and access controls, human oversight, AI safety, and secure AI deployment.\nExperience operating in customer-facing engineering, consulting, technical implementation, solutions architecture, or professional-services roles.\nStrong problem-solving skills and the ability to communicate complex technical concepts clearly to both technical and business stakeholders.\nAbility to manage ambiguity, prioritize effectively, travel approximately 25%, and deliver high-quality solutions in fast-moving customer environments.\nPreferred Qualifications\nAnthropic / Claude Experience\nHands-on experience with the Anthropic API, Claude models, Anthropic Console, Claude Code, or Anthropic implementation guidance.\nCompletion of relevant Anthropic Academy learning, partner enablement, technical training, or equivalent hands-on experience deploying Claude-based solutions.\nExperience applying Claude capabilities such as long-context processing, tool use, structured outputs, document analysis, and enterprise knowledge workflows.\nFamiliarity with Model Context Protocol (MCP) concepts and experience building or integrating secure tools and data connections for AI applications.\nServiceNow Experience - Strong Plus\nExperience with ServiceNow development, configuration, workflow automation, or enterprise platform implementation.\nFamiliarity with ServiceNow scripting, APIs, IntegrationHub, 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