Gen AI Engineer With Azure DevOps

Titel: Gen AI Engineer With Azure DevOps
Location: Dallas, TX

Mandatory Skills: Azure DevOps,GEN AI, LLMs, RAG, Agents, Agentic AI , LLM, Langsmith , AKS, Azure Container, Azure mOnitor

Responsibilities: -

Azure Devops
AKS, Azure Container, Azure mOnitor, LLM, Langsmith
RAG Pipeline, AgenticAI
Design, implement, and manage CI/CD pipelines for GenAI applications using Azure DevOps, GitHub Actions, or similar tools.
Automate infrastructure provisioning using Infrastructure as Code (IaC) tools like Terraform, Bicep, or ARM templates.
Deploy and manage containerized AI workloads using Azure Kubernetes Service (AKS), Azure Container Apps, or Azure ML.
Monitor and optimize performance of GenAI applications using tools like Azure Monitor, Application Insights, and LangSmith.
Implement robust security and compliance practices for AI workloads, including identity management, data encryption, and network security.
Collaborate with AI/ML engineers and solution architects to ensure smooth integration and deployment of LLMs, RAG pipelines, and agentic workflows.
Manage Azure resources such as Azure Blob Storage, Azure Cognitive Search, Azure OpenAI, and Azure Key Vault.
Support model versioning, deployment, and rollback strategies for LLMs and fine-tuned models.
7-12 years of relevant experience
Bachelor's and/or master's degree in computer science or equivalent experience.
Strong communication, analytical and problem-solving skills with a high attention to detail.

Skills: -

Mandatory skills

Gen AI Application engineering using RAG, Agents, Agentic AI and Monitoring Dashboards (LangSmith/LangGraph experience a plus)
Experience with LLMs (e.g., Open AI, GPT, LLaMA, Claude) deployments and fine-tuning techniques.
Prompt Engineering -Designing effective prompts for LLMs. Understanding of few-shot, zero-shot, and chain-of-thought prompting.
Experience with vector databases (Chroma, Pinecone, FAISS etc) and deploying RAG pipelines and CI/CD
Applied experience in cloud development - Azure, AWS or Google Cloud Platform Cloud.

Good to have skills: -

Experience with LangChain, LangSmith, or similar GenAI frameworks.
Knowledge of LLM deployment patterns, including model hosting, inference optimization, and scaling.
Familiarity with Azure OpenAI Service, Azure ML, and vector databases.
Exposure to multi-cloud or hybrid cloud environments.