About the Role
Our client, J-RAM IT Consulting Inc., is seeking a hands-on AI Native Software Engineer to design, build, and deploy production-grade AI-driven systems within complex enterprise environments. This role focuses on agent-based architectures, AI platform integration, and cloud-native development, delivering scalable, reliable solutions that power real business workflows. This is a 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, 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.