AI/ML Engineer
Indexed description
Job Title: AI/ML Engineer
Location: Dallas, TX
Employment Type: Long term contract
Key Responsibilities
- Design, develop, and deploy AI/ML models and GenAI applications in production environments.
- Build scalable ML/AI solutions using Python and modern ML frameworks.
- Develop and integrate LLM-based applications, RAG pipelines, AI agents, and MCP-based solutions.
- Implement Model Context Protocol (MCP) servers/tools and integrate AI agents with enterprise systems and APIs.
- Deploy and manage AI/ML workloads on Microsoft Azure and/or AWS.
- Containerize applications using Docker and deploy them on AKS/Kubernetes.
- Build and maintain CI/CD pipelines for AI/ML application and model deployments.
- Implement MLOps practices, including model versioning, automated testing, deployment, monitoring, and rollback.
- Develop cloud-native architectures using services such as Azure ML, Azure OpenAI, AKS, Azure DevOps and/or AWS SageMaker, EKS, Bedrock, Lambda, S3.
- Optimize AI/ML workloads for scalability, reliability, security, and cost.
- Integrate ML models with REST APIs, microservices, databases, and enterprise applications.
- Monitor model/application performance and troubleshoot production issues.
- Collaborate with DevOps and Cloud teams to implement infrastructure automation and Infrastructure as Code (IaC).
- Follow best practices for security, authentication, secrets management, logging, and observability.
Required Technical Skills
AI/ML & GenAI
- Strong programming experience in Python.
- Experience with Machine Learning / Deep Learning concepts and frameworks.
- Hands-on experience with Generative AI, LLMs, Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
- Experience with frameworks such as LangChain, LangGraph, LlamaIndex or similar.
- Understanding of model evaluation, optimization, and production deployment.
MCP – Model Context Protocol
- Hands-on knowledge of Model Context Protocol (MCP).
- Experience developing or integrating MCP servers, tools, resources, and clients.
- Ability to connect LLM/AI agents with enterprise APIs, databases, files, and external services using MCP.
- Understanding of secure and scalable MCP-based architectures.
Cloud – Azure / AWS
Strong experience with at least one cloud platform:
Azure:
- Azure Machine Learning
- Azure OpenAI
- Azure Kubernetes Service (AKS)
- Azure DevOps
- Azure Storage
- Azure Functions
- Azure Key Vault
- Azure Monitor
AWS:
- Amazon SageMaker
- Amazon Bedrock
- Amazon EKS
- S3
- Lambda
- IAM
- CloudWatch
- API Gateway
Kubernetes / AKS
- Hands-on experience with Kubernetes and AKS.
- Docker containerization.
- Kubernetes deployments, services, ingress, ConfigMaps, Secrets, and autoscaling.
- Experience deploying and managing AI/ML workloads in Kubernetes environments.
- Understanding of Helm and Kubernetes networking is preferred.
CI/CD & DevOps
- Strong experience building CI/CD pipelines.
- Experience with Azure DevOps, GitHub Actions, Jenkins, GitLab CI/CD, or similar.
- Automated build, test, security scanning, containerization, and deployment.
- Git/GitHub/GitLab/Bitbucket.
- Experience with Terraform, Bicep, or CloudFormation is preferred.
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