AI Engineer

The Forward Deployed Engineer: Agentic AI designs, builds, and deploys production-grade multi-agent systems within regulated enterprise environments. This role is responsible for developing scalable AI applications, retrieval pipelines, orchestration frameworks, APIs, observability tooling, and production infrastructure while working closely with Architects, Delivery teams, and enterprise client stakeholders. The role requires strong hands-on engineering capability, production delivery experience, and a strong understanding of enterprise AI engineering practices. At OBZ, all engineers are expected to use AI tools effectively in their daily work. AI assistants and workflow automation tools are expected to improve engineering productivity, testing, debugging, documentation, and delivery execution. What You Will Do

• Design and deliver production-grade multi-agent systems using orchestration frameworks such as LangGraph, LangChain, CrewAI, or equivalent technologies.

• Build and maintain end-to-end RAG pipelines including chunking strategies, embedding evaluation, retrieval optimization, reranking, and hallucination evaluation.
• Develop prompt frameworks, role instructions, escalation flows, and guardrail mechanisms for enterprise AI applications.
• Implement production-grade AI guardrails including PII redaction, input sanitization, output validation, adversarial testing, and security controls.
• Design and deploy AI systems within in-region and in-VPC enterprise environments using AWS services and regulated deployment architectures.
• Build and maintain FastAPI services, streaming APIs, authentication layers, React-based interfaces, and enterprise integrations.
• Deploy and manage workloads on ECS, EKS, SageMaker, Bedrock, API Gateway, and related AWS infrastructure services.
• Implement CI/CD pipelines, automated testing workflows, infrastructure automation, observability tooling, and production monitoring frameworks.
• Define and execute testing strategies including unit testing, integration testing, retrieval testing, adversarial testing, and evaluation frameworks.
• Contribute to architecture reviews, low-level designs, solution documentation, and technical proposal responses.
• Lead technical workstreams within engagements and support junior engineering team members through reviews, debugging, and technical guidance.
• Contribute reusable engineering assets including runbooks, prompt libraries, evaluation frameworks, deployment templates, and tooling accelerators.

• 4 - 6 years of experience in software engineering, AI engineering, cloud engineering, or backend platform development.
• Strong Python engineering capability with experience building production-grade backend systems.
• Hands-on experience building AI applications using LLM APIs, RAG systems, orchestration frameworks, or multi-agent workflows.
• Experience with LangGraph, LangChain, CrewAI, or similar agentic AI orchestration frameworks.
• Strong understanding of retrieval systems, embeddings, vector databases, reranking, chunking strategies, and AI evaluation methodologies.
• Experience deploying applications on AWS using services such as Bedrock, SageMaker, ECS/EKS, API Gateway, Cognito, CloudWatch, IAM, Secrets Manager, and related services.
• Strong understanding of DevSecOps practices including CI/CD, infrastructure automation, testing frameworks, observability, and security hygiene.
• Familiarity with React, Node.js, streaming APIs, and modern web application integration patterns.
• Understanding of enterprise AI risks including hallucinations, prompt injection, data leakage, and operational guardrails.
• Ability to work directly with client stakeholders, delivery teams, and architecture teams in enterprise delivery environments.

• Strong debugging, problem-solving, and engineering ownership capability. What Sets You Apart

• You have delivered production AI systems used by real users or enterprise environments.
• You actively use AI tools to improve engineering productivity, debugging, testing, and documentation quality.
• You are comfortable troubleshooting production issues and improving system reliability.
• You contribute reusable tooling, frameworks, or technical assets adopted across teams.
• You combine strong engineering execution with effective stakeholder communication.