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 Email -