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