Senior AI Solutions Engineer, Business Transformation
$135,000–$160,000 year
On-siteParamus, New Jersey, United States
Paramus, New Jersey, United StatesOn-siteFull Time$135,000–$160,000 yearSenior LevelMedium
Full TimeSenior LevelMedium
Job Summary
Guide business teams in selecting the right AI application path, from conversational tools to custom assistants and no-code development, by judging whether colleagues can build independently or require engineering support. Lead a portfolio of concurrent projects defining technical stacks for LLM-based applications, RAG pipelines, and multi-agent workflows, while reviewing non-engineer solutions to ensure production readiness. Communicate technical reasoning to business functions, IT, and leadership, coordinating with Korea-based teams across time zones. Operate as the primary on-site technical presence for AI transformation in Commercial, Supply Chain, and Staff functions, deploying services to cloud or on-premises environments under enterprise constraints.
Required Qualifications
- Bachelor's degree or higher in Computer Science, Engineering, or a related field, or equivalent practical experience
- Minimum of 10 years in software engineering, AI service development, or technology consulting
- At least 3 years building generative AI and LLM-based services
- At least 2 years leading projects as the accountable owner
- Demonstrated ability to lead a portfolio of concurrent projects—intake, prioritization, scoping, scheduling, stakeholder alignment, risk escalation, and outcome reporting
- Practical command of the range of AI adoption paths available to non-developers—conversational AI tools, customized assistants, and no-code and low-code development—with the judgment to match each business need to the right approach
- Ability to review solutions built by non-engineers, assess production readiness, and raise them to an operable standard
- Ability to define the technical stack and architecture for a project and drive it through to delivery, working with others where needed
- Strong proficiency in Python, with the ability to design, implement, and debug independently
- SQL proficiency sufficient for data querying and transformation
- Hands-on work with prompt engineering, retrieval-augmented generation (RAG), agentic AI systems, and multi-agent orchestration frameworks
- Experience deploying an AI service used regularly by real users to a cloud (AWS or Azure) or on-premises environment and operating it in production, not limited to PoC or prototype stages
- Ability to build quickly with AI coding tools and to validate and refactor the generated code into production-ready form, with full responsibility for understanding, validating, and maintaining every piece of delivered code
- Working command of core software engineering practices: version control, containerization, CI/CD, and automated testing
- Technical communication and enablement: Ability to explain the reasoning behind technical judgments in language non-engineers understand, to guide colleagues toward improving their own work, and to say no to an approach constructively while offering a viable alternative. Comfort communicating in every direction—with business functions, IT, and leadership
- Learning and collaboration: Ability to quickly learn complex, multi-domain business environments—Commercial, SCM, and Staff functions such as Legal, HR, and Finance—and to work alongside those teams to connect their needs to practical technical solutions
- Comfort operating as the primary on-site technical presence for AI transformation work at this site
- Willingness to coordinate with Korea-based team members as projects require, including occasional meetings scheduled across time zones
- Professional-level English communication skills are required, including the ability to lead meetings and negotiate with business stakeholders
- Work Authorization: Applicants must be legally authorized to work in the United States. Visa sponsorship is not available for this position
Desired Qualifications
- Experience designing and running AI literacy or technical training programs for non-engineering audiences, and measuring their adoption outcomes
- Experience bringing solutions built by business users with AI or no-code tools into production
- Consulting, systems integration, or agency background with exposure to many organizations and customer environments
- Experience embedding observability (logging, metrics, alerting) into production services and using those signals to improve system architecture
- Experience building and deploying services under enterprise constraints such as firewalls, proxies, and corporate authentication systems; familiarity with SSO, EAI, and API Gateway, and with IT infrastructure fundamentals (APIs, authentication, networking, and security)
- Snowflake access control and data governance design; pipeline scheduling, dependency, and failure-handling design (Snowflake Tasks, dbt, Airflow, and similar); familiarity with MLOps concepts
- Experience reviewing and managing deliverables from external vendors or outsourced partners
- Projects launched and operated across multiple business domains, described with technical stack, scope of ownership, and operational outcomes
- Mentoring junior engineers or leading technical workstreams
- Regulated industry experience — biopharma, healthcare, or similar
- A track record across diverse companies, projects, and customers is preferred over deep specialization in a single domain
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