Remote: Agentic AI Engineer

Dice is the leading career destination for tech experts at every stage of their careers. Our client, J-RAM IT Consulting Inc., is seeking the following. Apply via Dice today! 10+ exp required We are seeking a hands-on AI Native Software Engineer to design, build, and deploy production grade AI driven systems within complex enterprise environments. In this role, you will focus on agent-based architectures, AI platform integration, and cloud native development, delivering scalable, reliable solutions that power real business workflows. This is 100% hands-on engineering role, ideal for a senior technologist who thrives at the intersection of AI systems, software engineering, and cloud infrastructure. Key Responsibilities: Core Duties: Design, implement, and maintain AI agent workflows, including retrieval augmented generation (RAG), orchestration, tool/function invocation, and policy-based routing Build cloud native backend services and APIs to support AI driven applications and enterprise integrations Implement evaluation, monitoring, and observability frameworks to ensure accuracy, latency, reliability, and system health across AI agent lifecycles Optimize AI and system performance across cost, scalability, and latency dimensions in production environments Deliverables or Project Scope: Production ready AI powered applications aligned to defined business workflows and enterprise standards Scalable multi model and multi provider AI architectures, including abstraction layers for provider flexibility Fully deployed cloud native services using microservices, containers, and serverless or event driven patterns Robust CI/CD pipelines, infrastructure as code implementations, logging, monitoring, and fault tolerant deployments Collaboration Tools or Platforms: Microsoft Office (Excel, Word, Outlook, Teams) AI Platforms & Models: OpenAI, Anthropic (Claude), Google Vertex AI, and select open source models Agent & Orchestration Frameworks: LangGraph, AutoGen, CrewAI (or similar) Cloud & DevOps Tooling: Docker, Kubernetes, Terraform, Helm, CI/CD pipelines Enterprise Integration: APIs, enterprise platforms, monitoring and observability tools Why You'll Love This Role: Build real, enterprise grade AI systems that move beyond experimentation into production Remain deeply technical in a 100% hands on engineering role with no people management responsibilities Work with modern AI platforms, multi model architectures, and cloud native technologies Focus on high impact delivery with clear scope, measurable outcomes, and implementation ownership Collaborate with experienced engineering teams in an execution driven environment Qualifications: Bachelor's degree in Computer Science, Engineering, or a related technical field or equivalent practical experience 8-10+ years of professional software engineering experience with ownership of production systems 3+ years of hands-on experience building and deploying AI/LLM based systems in production (agents, RAG pipelines, orchestration) Strong experience designing and delivering cloud native systems, including APIs, microservices, containers, and serverless or event driven architectures Proficiency in Python, Java, or comparable backend languages Hands on experience with CI/CD pipelines, infrastructure as code, and monitoring or observability tools Proven ability to deliver production quality code, including testing, debugging, performance tuning, and operational readiness Preferred Qualifications: Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar Experience designing multi agent or distributed AI systems Familiarity with multi model and multi provider AI architectures Experience integrating AI solutions into enterprise scale systems or platforms Demonstrated experience optimizing AI workloads for cost, performance, and latency Additional Information/Requirements: This is 100% hands on engineering role with no people management responsibilities Strong problem-solving skills and technical judgment in complex enterprise environments Ability to collaborate effectively with internal and client engineering teams Comfortable working within existing architecture standards, security requirements, and engineering best practices Strong written and verbal communication skills for technical documentation and design discussions