Agentic Solutions Engineer

BMG360 • Shelton, CT

3d

About The Position

We’re hiring an Agentic Solutions Engineer to be the hands-on technical backbone supporting our Agentic Solutions Architect and the broader delivery team. This is a builder’s role: you design, develop, and maintain the API-first, MCP-enabled infrastructure that powers our autonomous marketing workflows and AI-driven client solutions.

You’ll architect and implement the service layer that connects everything—MCP servers, REST APIs, Django backends, and React-powered admin interfaces—enabling the Architect to design agentic systems that actually ship. If you love writing clean Python and TypeScript, building reliable API infrastructure, and want to work at the bleeding edge of AI tooling, this role was built for you.

This is NOT a generalist DevOps or data engineering role. You specialize in API-first design, MCP protocol implementation, and full-stack internal tooling for agentic systems. You are the engineer who makes the Architect’s blueprints real.

Perfect for someone with a strong backend and full-stack background who is passionate about applying cutting-edge AI tooling to solve real-world marketing technology problems.

Requirements

• 3–6 years in backend or full-stack engineering roles, ideally at an agency, SaaS, or data/AI company.
• Expert-level Python: you write clean, tested, idiomatic Python for APIs, data pipelines, and LLM integrations daily.
• Proficient TypeScript/JavaScript: comfortable in both Node.js server contexts and React frontend work.
• Hands-on experience building and consuming REST APIs—you understand OAuth2, JWT, API versioning, pagination, and webhook security.
• React experience: you can build functional, component-driven UIs and are familiar with hooks, state management, and API integration patterns.
• Familiarity with relational databases (PostgreSQL preferred) and ability to write efficient queries and design schemas.
• Understanding of async patterns: event queues, background workers, webhooks, polling.
• Python: FastAPI, Django/DRF, Celery, Pydantic, httpx, pytest—production-grade Python is your default.
• TypeScript/JS: Node.js APIs, React (hooks, context, functional components), type-safe interfaces, module patterns.
• Django: models, migrations, DRF serializers, viewsets, permissions, admin customization, Celery task integration.
• React-Admin: custom resources, data providers, list/show/edit views, auth integration—or strong React with a fast ramp.
• APIs: OpenAPI spec-first design, Swagger/Redoc documentation, rate limiting, CORS, versioning strategies.
• MCP Protocol: familiarity with Model Context Protocol preferred; hands-on MCP server development is a major plus.
• Cloud/infrastructure: AWS or GCP (Lambda, Cloud Run, S3/GCS), Docker, basic CI/CD pipelines (GitHub Actions).
• Data: PostgreSQL, Snowflake familiarity; ETL patterns; ability to write complex SQL.

Nice To Haves

• Familiarity with Model Context Protocol preferred; hands-on MCP server development is a major plus.
• Strong React with a fast ramp for React-Admin.

Responsibilities

• Design, build, and maintain RESTful and GraphQL APIs that serve as the backbone of agentic workflow systems.
• Implement Model Context Protocol (MCP) servers that expose tools, resources, and prompts to LLM agents—enabling Claude and other agents to interface with internal and external data and services.
• Develop webhook receivers, event-driven triggers, and real-time communication layers (WebSockets, SSE) to support autonomous agent pipelines.
• Ensure APIs are secure, versioned, rate-limited, and observable with proper logging and distributed tracing.
• Build and maintain the internal MCP server registry; keep tool schemas, descriptions, and input validation accurate and up to date.
• Build and extend Django-based backend services that power internal tools, client data pipelines, and agent orchestration APIs.
• Design PostgreSQL schemas for agentic workflow state, job queuing, and audit trails.
• Implement Django REST Framework (DRF) endpoints consumed by React frontends and external agent clients.
• Develop background task processing using Celery and Temporal or similar for async agent jobs, polling loops, and scheduled automations.
• Write Python integrations with LLM providers (Anthropic, AWS), marketing platform APIs (Meta, Google, GA4), and data warehouses (Snowflake).
• Build internal admin dashboards using React-Admin to surface agentic workflow status, inspect agent job runs, and manage MCP server configurations.
• Develop React-based monitoring and configuration UIs for the Architect and delivery teams.
• Create reusable React components and hooks for common UI patterns across internal tool surfaces.
• Integrate frontend tooling with Django/DRF backends and MCP-driven data sources.
• Develop and maintain a reusable Python + TypeScript SDK of pre-built agent tools—platform API wrappers, data warehouse clients, webhook handlers—so new MCP servers and workflows can be stood up faster.
• Support the Architect in prototyping and evaluating new agentic frameworks (Flowise, LangChain, custom orchestration).
• Document MCP servers, API contracts, and SDK usage so the broader engineering team can build on your infrastructure.
• Test and validate the accuracy and reliability of API outputs, MCP tool responses, and agentic workflow integrations.

Benefits

• Compensation is commensurate with experience.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed