Skaled Consulting · 3 hours ago
AI Engineer
United States
Full-time
Remote
Senior Level
$160K/yr - $190K/yr
5+ years expSkaled Consulting is a Revenue Performance Agency that empowers B2B companies to scale smarter through AI and data solutions. The AI Engineer will be responsible for developing custom integrations and complex back-end solutions, ensuring the scalability and performance of AI systems in enterprise contexts.
ConsultingInformation TechnologyLead GenerationMarketingTraining
Responsibilities
Custom Integration Development: Write code to connect AI assistants with systems or data sources when a custom approach is required. This could mean developing a microservice or script to interface with a proprietary database, creating a new API endpoint to expose data for an AI agent, or building a plug-in/connector for a platform that lacks one. The Engineer uses languages like Python (or Node/Java depending on the stack) to build these integrations, ensuring they are secure and efficient
Platform-Level AI Implementations: Lead the deployment of AI solutions on enterprise-grade platforms. For example, containerizing an AI service and deploying it on Azure (possibly Azure OpenAI or Azure Functions), Google Cloud Vertex AI, or even on-premise environments as needed. They handle the setup of environments, whether it’s provisioning an AI model endpoint, setting up a vector database (for storing embeddings), or configuring cloud functions that host the logic created by the Builder in a more scalable way
Database & Data Pipeline Integration: Develop and manage data flows that support AI solutions. This might involve writing data extraction and transformation jobs to feed data into an AI model (like syncing CRM data to a vector store such as Pinecone or an Azure Cognitive Search index). The Engineer ensures data needed for AI is available and updated as required. They also handle writing results back to databases or data warehouses if the AI solution needs to log outputs or enable analytics
Performance Optimization: Analyze and improve the performance of AI workflows. This includes optimizing response times of AI agents (perhaps by implementing caching layers, batching calls, or fine-tuning model parameters), ensuring that integrations handle high volumes (multi-threading or async processing for concurrency), and monitoring system metrics (CPU, memory, API throughput) to preemptively scale resources. The Engineer sets up logging and monitoring for the technical components, so issues like slowdowns or errors can be detected and addressed
Security & Compliance Implementation: Ensure that all custom components and deployments adhere to security best practices and any client-specific compliance requirements. For instance, handling API keys and credentials securely (using vaults or key management services), ensuring data in transit and at rest is encrypted, implementing user authentication/authorization for any custom APIs, and complying with data privacy rules (not logging sensitive data from AI interactions, etc.). The Engineer often interfaces with the client’s IT/security teams to get approvals (e.g., having code security reviewed, or meeting penetration testing requirements)
Technical Troubleshooting & Support: Act as the highest escalation point for technical issues. If an AI integration is failing or an assistant is behaving unexpectedly due to technical reasons, the Engineer dives into logs, traces through code, and pinpoints the root cause. They fix bugs in custom code, handle incidents (like a service outage affecting an AI component), and ensure restoration of service. In the course of support, they may also implement improvements to prevent future issues (building more robust error handling, or adding redundancy)
Technical Strategy & Tooling: Advise on and implement tools/frameworks to improve the practice’s tech stack. For example, evaluating whether to use a framework like LangChain for managing prompts and context, deciding on the best vector database solution, or setting up CI/CD pipelines for code deployments. The Engineer contributes to the technical architecture decisions of the practice and might build internal utilities (scripts, libraries) that speed up development for the team (like a template for calling the OpenAI API with certain retry logic, which Builders can then use)
Qualification
Required
5–10+ years of experience in software development or engineering roles, with a proven track record of building integration-heavy and complex backend systems
Deep expertise in Cloud & DevOps practices, including hands-on experience deploying applications on Azure, Google Cloud (Vertex AI), or AWS, and proficiency with containerization (Docker/Kubernetes) and setting up CI/CD pipelines
Expert knowledge of APIs and System Integration principles, specifically in RESTful API design, webhooks, and secure authentication methods (OAuth, API keys)
Practical experience in the AI/ML domain, including implementing AI APIs, deploying models, and working with relevant frameworks and tools like LangChain and vector databases (e.g., Pinecone)
Strong System Architecture and Security mindset, capable of designing scalable, reliable architectures, and a commitment to secure coding practices and compliance implementation
Company
Skaled Consulting
Skaled is a Revenue Performance Agency dedicated to helping B2B companies scale smarter by aligning strategy with execution, powered by AI, guided by data, and executed by industry-leading experts.
11-50 employees
Funding
Current Stage
Early StageLeadership Team
Jake Dunlap
Chief Executive Officer
Recent News
Business Strategy Hub
2023-12-22
2023-12-22
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