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