Senior Agentic AI Software Engineer

Paramount•New York, NY

3d•$124,000 - $186,000

About The Position

The Paramount Technology department supports our global content company by delivering a high-quality viewing experience, supporting system infrastructure, and creating innovative ways for our employees to collaborate. If you are interested in a strategy focused on growth, transformation, and ongoing innovation where you can help “reinvent the broadcasting wheel,” Paramount Tech has a role for you. Paramount Tech is seeking a Senior Agentic AI Software Engineer to architect and build the next generation of our business-critical systems. This role focuses on modernizing our core engineering ecosystem using agentic AI and modern AI protocols, bridging robust legacy foundations with automated, intelligent workflows. We are looking for skilled engineers who understand the craft of building, deploying, and maintaining business-critical enterprise applications. While agentic AI continues to accelerate delivery, it does not replace the creativity and judgment required to ship applications at this scale. Whether you are grounded in Python, .NET, or Node.js, you should know how to construct, troubleshoot, deploy, and maintain top-performing applications in a dynamic delivery environment while continuously shipping new features without disrupting business operations.

Requirements

5+ years of enterprise software development experience, including deep experience with .NET, Python, or Node.js in a business-critical setting.
3+ years of experience with Angular and TypeScript.
Proven hands-on experience using AI development tools such as Cursor, GitHub Copilot agent mode, and/or Claude Code on real production codebases, with clear discipline around review, testing, and code ownership.
Working knowledge of MCP for integrating AI tools with local and remote data sources.
Ability to translate complex technical and AI concepts for non-technical stakeholders and executive management.

Nice To Haves

Proven experience with Azure AI Services, AWS Bedrock AgentCore, Google Vertex AI Agent Builder, or similar agent frameworks.
3+ years of experience with database modeling, storage, and retrieval using SQL and/or NoSQL, including writing and maintaining complex queries.
Experience with Azure DevOps or equivalent build and release pipelines.
Deep knowledge of system prompts, chain-of-thought prompting, planning and agent modes, and “rules” files used to govern AI behavior.
Experience specifically migrating systems using agentic AI.
Active contributions to open-source AI projects or frameworks.
Advanced certifications in Azure AI or cloud architecture.
Experience with RPA tools such as Automation Anywhere, UiPath, or similar enterprise automation platforms.
Experience with batch scheduling, queuing, or integration technologies.
Experience with multi-region or multi-zone cloud configurations.
Experience with multi-agent systems, orchestration, or RAG workflows.
Experience with application telemetry and observability.
A background in media technology.

Responsibilities

Deploy agentic workflows and MCP-enabled tools to bridge legacy APIs with modern application services.
Use agentic AI tools and MCP to automate boilerplate, refactoring, and the porting of legacy systems to an Angular front end backed by Python, .NET, or Node.js, leveraging native cloud services where appropriate.
Transform natural-language requirements and user stories into executable specifications, intent-based tasks, and acceptance criteria while automatically generating supporting documentation, diagrams, and impact analyses.
Use high-speed AI iteration to explore multiple architectural directions in parallel, shifting from concept to draft PR with minimal overhead.
Implement AI-driven testing agents for unit, integration, and property-based tests.
Lead human-in-the-loop reviews of AI-generated commits while keeping architectural integrity, security guardrails, and compliance front and center.
Curate codebase context, including indexing, agent rules, and MCP servers, and optimize developer-facing AI tools to minimize token usage and latency while maximizing generation accuracy.
Apply OWASP standards and additional security practices related to data and IP protection, encryption, secure key storage and rotation, and defense against internal and external threats.
Estimate effort and milestones using AI-assisted planning.
Translate delivery metrics into status updates that executive stakeholders can act on.