Role - AI/ML Engineer
Location: Remote
Fulltime
Role Summary
looking for contractor who is highly proficient in Python and has practical experience developing AI-powered systems. The candidate should have worked with AI agents, Model Context Protocol (MCP), modern data management techniques,.
Core Responsibilities
•Design, build, and maintain Python based services and automation workflows
•Implement MCP for agent communication, control, and observability
•Build, transform, and manage data pipelines supporting AI and analytics use cases
•Deploy, monitor, and optimize solutions in cloud environments
•Collaborate with product, data, and engineering teams to deliver end-to-end solutions
•Ensure code quality, performance, security, and maintainability
Required Technical Skills
•Python: Advanced proficiency; production experience with APIs, async processing, and testing
•AI / LLM Agents: Experience designing and implementing autonomous or semiautonomous AI agents (e.g., tool using agents, planners, orchestrators)
•MCP (Model Context Protocols): Experience with agent communication, coordination frameworks, or protocol driven AI architectures
•Data Management:
•Data modeling and data pipelines
•Working with SQL and NoSQL databases
•Experience with data quality, governance, and large scale datasets
•Cloud Experience:
•Hands on work in at least one major cloud platform (Azure, AWS, or GCP)
•Experience with cloud storage, compute, and managed services
•Familiarity with CI/CD and cloud native deployment patterns
Preferred / Nice to Have
•Experience with vector databases and embeddings
•Familiarity with MLOps or LLMOps practices
•Experience with streaming data or event driven architectures
•Knowledge of security and compliance considerations for AI systems
Engagement Characteristics
•Experience collaborating with remote and cross functional teams
Technical Skills
•AI/LLM Agent and MCP (Model Control Protocols) – Google ADK, Copilot Studio
•Cloud Experience – Google Cloud or Azure preferred.
•Database Knowledge – BigQuery, Firestore, Cloud SQL, etc.
•Data pipeline – Dataflow
•Power Automate
•Automation Tooling – UI Path, etc.
•CI/CD Pipeline – Azure DevOps Pipeline
•Infrastructure as Code (IaC) - Terraform
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