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

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

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