Agentic AI Software Engineer - Remote (75% travel potential)

Agentic AI Software Engineer - Remote (75% travel potential)

Posted 4 days 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 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 Systems Integration Terraform Test Strategy Web Services Workflows

Summary: The Agentic AI Software Engineer role at Cerebra Consulting Inc involves developing and implementing AI-driven software solutions, with a focus on cloud-native systems and AI platforms. The position requires extensive experience in software engineering, particularly in building and deploying AI systems in production. Candidates should be proficient in backend programming languages and familiar with modern development practices such as CI/CD and Infrastructure as Code. The role offers remote work with significant travel potential, supporting the client's expanding AI practice.

Key Responsibilities:

  • Design and implement AI agents, including retrieval (RAG), orchestration workflows, tool/function invocation, and policy-based routing.
  • Build evaluation frameworks for accuracy, latency, and reliability.
  • Implement observability and monitoring for agent lifecycle.
  • 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.
  • Develop scalable services using microservices architecture, containers, and serverless patterns.
  • Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm).
  • Ensure production readiness, logging, monitoring, and fault tolerance.
  • Build and deploy AI-powered applications aligned to business workflows.
  • Integrate AI systems into existing enterprise platforms and APIs.
  • Define and execute test strategies for AI systems and measure system performance.
  • 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 and Infrastructure as Code.
  • Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar).
  • 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.

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.

Hello,

Hope you are doing good!!

Position: Agentic AI Software Engineer (only w2)

Location: Remote (75% travel potential)

Duration: Long-Term

Client is 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

Thanks,

Sudhanshu Srivastava

Email -

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