About the Role
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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