AI Engineer

Detailed JD Below.

• *Forward Deployed Engineer (AI, Hyperscaler Cloud \& DevOps)**

• *Role Summary**

We are seeking a hands\-on **Forward Deployed Engineer (FDE)** with expertise in **Generative AI, Hyperscaler Cloud Platforms, DevOps, and Platform Engineering**. The role involves designing, deploying, integrating, and operationalizing AI agents and cloud\-native solutions across enterprise environments while ensuring reliability, scalability, security, and operational excellence.

• *Key Responsibilities**

• Design, develop, and deploy AI/GenAI agents using Agentic AI frameworks and LLM technologies.
• Build and operationalize agent workflows for automation, incident response, RCA, deployment validation, and reliability engineering.
• Deploy and manage AI solutions across **hyperscaler cloud platforms (Azure, AWS, GCP)** and Kubernetes environments.
• Integrate agents with CI/CD pipelines, observability tools, ITSM platforms, code repositories, and enterprise systems.
• Develop reusable APIs, connectors, prompt templates, and automation frameworks.
• Implement AI governance controls including guardrails, access controls, audit logging, monitoring, cost optimization, and rollback mechanisms.
• Own deployment, troubleshooting, observability, and continuous improvement of AI agent platforms.
• Collaborate with DevOps, SRE, Security, Platform, and Engineering teams to deliver production\-ready solutions.

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• *Required Skills \& Experience**

• 5\+ years of experience in Software Engineering, Cloud Engineering, DevOps, SRE, AI/ML Engineering, or related roles.
• Strong programming skills in **Python** with experience building AI/GenAI applications.
• Hands\-on experience with **LangChain, LangGraph**, Agentic AI frameworks, and LLM technologies.
• Experience deploying AI/GenAI solutions in production environments.
• Strong understanding of:
• Prompt Engineering
• RAG
• Vector Databases
• Tool Calling
• Agent Orchestration
• Structured Outputs
• Strong experience with one or more **Hyperscalers: Azure, AWS, or GCP**.
• Hands\-on experience with:
• Kubernetes (AKS/EKS/GKE)
• Docker
• Terraform
• GitHub Actions / Azure DevOps / Jenkins
• CI/CD and Infrastructure as Code (IaC)
• Experience with observability and monitoring tools such as Grafana, Prometheus, OpenTelemetry, Splunk, Dynatrace, or Datadog.
• Understanding of DevOps, SRE, cloud reliability, security, and operational best practices.

• *Preferred Skills**

• Multi\-cloud architecture and deployment experience across Azure, AWS, and GCP.
• Experience with AI Reliability Engineering, LLMOps, MCP, Agent Gateways, and Responsible AI practices.
• Exposure to .NET or Java application development.
• Experience working in enterprise or regulated environments.
• Certifications in Azure, AWS, GCP, Kubernetes, AI Engineering, or DevOps.

• *Behavioral Attributes**

• Strong ownership mindset with a "get things done" attitude.
• Excellent problem\-solving and multitasking abilities.
• Ability to work directly with client and platform teams.
• Curious, adaptable, and eager to learn emerging AI and hyperscaler technologies

Benefits

• Health insurance

Work Location: In person