Agentic AI Software Engineer

Agentic AI Software Engineer

Posted 1 day ago by Cerebra Consulting Inc

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Negotiable
Undetermined
Remote
Remote or Pennsylvania
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Abstraction Layers Amazon Elastic Block Stores Amazon Web Services (AWS) Application Development Application Programming Interface (API) Artificial Intelligence Azure Kubernetes Service Big Data Business Analytics Cloud Computing Cloud Technologies Cloud Technology Configurators Continuous Integration and Continuous Delivery Generic Programming Infrastructure as Code (IaC) Java (Programming Language) Kubernetes Microservices Mobile Application Development Open Source Development Oracle Hyperion PeopleSoft Customer Relationship Management (CRM) Python (Programming Language) Salesforce SAP Product Lifecycle Management (PLM) Software Development Software Engineering System Deployment Systems Integration Terraform Test Strategy Web Services Workflows

Summary: The Agentic AI Software Engineer role at Cerebra Consulting Inc involves designing and implementing AI agents and cloud-native systems to support the client's AI practice. The position requires extensive software engineering experience, particularly in AI and cloud technologies, and includes responsibilities such as integrating AI platforms and developing scalable services. The role is remote with a potential for 10% travel and is part of a long-term contract with multiple openings available. Candidates should possess strong technical skills in backend programming and AI system deployment.

Key Responsibilities:

  • Design and implement AI agents, including retrieval, orchestration workflows, tool/function invocation, policy-based routing, and evaluation frameworks for accuracy, latency, and reliability.
  • Integrate with AI providers and build abstraction layers to support multi-model and multi-provider architectures.
  • Develop scalable services using microservices architecture, containers, serverless patterns, and implement CI/CD pipelines and infrastructure as code.
  • Build and deploy AI-powered applications aligned to business workflows and integrate AI systems into existing enterprise platforms and APIs.
  • Define and execute test strategies for AI systems, measure system performance, and debug and optimize production systems.

Key Skills:

  • 8-10+ years of software engineering experience.
  • Strong experience with cloud-native systems (APIs, microservices, containers, serverless).
  • Experience building and deploying AI/LLM-based systems in production.
  • Proficiency in Python, Java, or similar backend languages.
  • Experience with CI/CD pipelines, Infrastructure as Code, monitoring and observability tools, and hands-on experience with AI platforms.
  • Experience with agent frameworks and designing multi-agent or distributed AI systems.
  • Familiarity with enterprise-scale system integration and optimizing AI workloads for cost and performance.

Salary (Rate): undetermined

City: undetermined

Country: undetermined

Working Arrangements: remote

IR35 Status: undetermined

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:
Cerebra Consulting Inc is a System Integrator and IT Services Solution provider with a focus on Big Data, Business Analytics, Cloud Solutions, Amazon Web Services, Salesforce, Oracle EBS, Peoplesoft, Hyperion, Oracle Configurator, Oracle CPQ, Oracle PLM and Custom Application Development. Utilizing solid business experience, industry-specific expertise, and proven methodologies, we consistently deliver measurable results for our customers. Cerebra has partnered with leading enterprise software companies and cloud providers such as Oracle, Salesforce, Amazon and able to leverage these partner relationships to deliver high-quality, end-to-end customer solutions that are targeted to the needs of each customer.

Title: Agentic AI Software Engineer

Location: Remote (10% travel potential)

Number of Openings: 10 (initial wave then 10 more)

Duration: Long-Term Contract

looking for multiple AI Native Software Engineers to support our client's growing AI practice!

What You Must Have

  • 8 10+ years of software engineering experience
  • Strong experience with cloud-native systems (APIs, microservices, containers, serverless)
  • Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration)
  • Proficiency in Python, Java, or similar backend languages
  • Experience with:
    • CI/CD pipelines
    • Infrastructure as Code
    • Monitoring and observability tools
    • Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar)

What We'd Like You to Have

  • Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
  • Experience designing multi-agent or distributed AI systems
  • Familiarity with enterprise-scale system integration
  • Experience optimizing AI workloads for cost and performance

Responsibilities Will Include

  • Design and implement AI agents, including:
    • Retrieval (RAG)
    • Orchestration workflows
    • Tool/function invocation
    • Policy-based routing
    • Build evaluation frameworks for accuracy, latency, and reliability
    • Implement observability and monitoring for agent lifecycle
  • AI Platform Integration
    • Integrate with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models)
    • Build abstraction layers to support multi-model and multi-provider architectures
    • Optimize model usage for performance, cost, and latency
  • Cloud-Native Development
    • Develop scalable services using:
        • Microservices architecture
        • Containers (Docker, Kubernetes)
        • Serverless and event-driven patterns
        • Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm)
        • Ensure production readiness, logging, monitoring, and fault tolerance
  • Application Development
    • Build and deploy AI-powered applications aligned to business workflows
    • Integrate AI systems into existing enterprise platforms and APIs
    • Develop backend services and APIs supporting agent workflows
  • Testing & Performance
    • Define and execute test strategies for AI systems
    • Measure system performance (latency, throughput, accuracy, cost)
    • Debug and optimize production systems

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