C2C Role: Senior AI Solutions Developer at Rutherford, NJ

Hands-on developer with strong experience building AI-enabled applications, assistants, workflows, and integrations. You have strong software engineering fundamentals, deep Python experience, and the ability to connect systems, data, APIs, and services into practical working solutions.
You are comfortable working directly with business stakeholders, application teams, and domain experts. You can move from an ambiguous business problem to a working  prototype by understanding the workflow, systems, data, exceptions, and operational constraints involved.
You bring an agile mindset to an enterprise environment. You are comfortable building quickly, learning through prototypes, documenting what works, and helping shape repeatable patterns for a growing AI Center of Excellence. You understand that successful AI solutions require not only strong engineering, but also practical governance, risk, security, data access, evaluation, and adoption considerations.

What You'll Do
Prototype and Solution Development

• Work directly with business teams, application teams, and domain experts to understand workflows, pain points, systems, data, exceptions, and operational  constraints.
• Build rapid prototypes and proof-of-concepts for AI-enabled workflows, assistants,agents, automations, and features in close partnership with users and stakeholders, testing ideas against real business processes and enterprise environments.
• Rapidly validate what is technically feasible, what is operationally useful, and what should not move forward.
• Translate prototype learnings into solution recommendations, technical patterns, implementation considerations, and governance inputs for the AI CoE.
• Help move successful prototypes toward scalable delivery by partnering with application, architecture, data, security, and platform teams.

Engineering and Integration

•  Use Python, APIs, databases, and services to connect systems, data, documents, and workflows.
•  Integrate with enterprise systems while following security, privacy, architecture, and technology standards.
•  Work with authentication, authorization, secure data access, and permission aware integration patterns.
•  Design solutions that are practical, maintainable, and appropriate for enterprise use.

AI Application Development

• Work with LLM-based tools and platforms to build applications that support business workflows.
• Implement orchestration approaches for multi-step workflows, tool usage, and agentic patterns.
• Apply retrieval, structured data access, and workflow automation where appropriate.
• Build AI-enabled workflows that include appropriate human review, guardrails, error handling, and escalation paths.

Governance, Risk, and CoE Enablement

• Support practical governance, risk, and compliance considerations based on lessons learned from AI prototypes and proof-of-concepts.
• Help identify common risks, controls, review points, and documentation needs for AI-enabled solutions.
• Partner with security, privacy, legal, data, architecture, and governance stakeholders to support responsible AI solution development.

Evaluation and Observability

• Implement logging, testing, monitoring, and basic observability for prototypes and workflows.
• Use evaluation and tracing tools to measure performance, identify failure modes, and improve solutions.
• Support structured testing and iteration of AI-enabled solutions with business users and technical stakeholders.
• Help define practical evaluation approaches for accuracy, usefulness, reliability, latency, cost, risk, and user experience.

You'll Need to Have

• Bachelor’s degree or equivalent experience in Computer Science, Engineering, Information Systems, or a related field.
• 5 to 8 years of experience in software development, application development, systems engineering, or related engineering roles.
• Strong proficiency in Python.
• Strong experience with APIs, system integration, and service-based architectures.
• Solid knowledge of SQL and working with data in enterprise environments.
• Experience building AI-enabled applications, assistants, agents, automations, or workflows using modern LLM tools and platforms.
• Experience working directly with business stakeholders or end users to design, prototype, and iterate on software or AI-enabled solutions.
• Experience working with tool integration patterns, including exposing or consuming internal tools and services within application workflows.
• Understanding of orchestration approaches for multi-step workflows, tool usage, and agentic patterns.
• Experience implementing logging, testing, and basic observability.
• Understanding of authentication, authorization, secure data access, and enterprise integration considerations.
• Ability to identify practical risks, controls, dependencies, and trade-offs when developing AI-enabled solutions.
• Strong problem-solving skills and the ability to work through ambiguity.
• Strong written and verbal communication skills.
• Ability to manage multiple priorities in a fast-paced environment.

We'd Love to See

• Experience in a forward-deployed engineering, solutions engineering, applied AI  engineering, innovation lab, consulting engineering, or internal product/prototype  development environment.
• Experience building or working with MCP servers, for example using FastMCP or similar tools.
• Experience with orchestration frameworks such as LangGraph or similar tools.
• Experience with evaluation and observability platforms such as LangSmith, Weights & Biases, or OpenTelemetry-based tracing.
• Experience with retrieval patterns and working with structured and unstructured data.
• Familiarity with enterprise AI governance considerations, including privacy, security, data access, HITL design, and responsible use.
• Experience working with security, privacy, legal, architecture, data governance, or risk stakeholders.

Skills & Competencies

• Software engineering fundamentals
• Python development
• API and system integration
• Data and SQL proficiency
• AI application development
• Agentic workflow development
• Workflow orchestration
• Secure enterprise integration
• Rapid prototyping
• Technical feasibility assessment
• Testing, evaluation, and observability
• Governance, risk, and compliance awareness
• Business and stakeholder collaboration
• Clear technical communication
• Strong execution and ownership