AI Engineer

Agent Development & Architecture

• Build AI Agents from scratch, including reasoning loops, memory management, tool calling, and state handling
• Design and implement multi-agent systems with defined roles, handoffs, and coordination logic
• Build agent orchestration layers to manage task delegation, agent communication, and failure recovery
• Implement planning and reflection patterns (ReAct, Plan-and-Execute, etc.) for autonomous task completion
• Design agents capable of handling long-running, multi-step workflows with human-in-the-loop checkpoints

MCP Integration

• Build and maintain MCP (Model Context Protocol) servers and clients from the ground up
• Integrate MCP servers into agent runtimes to expose enterprise tools, APIs, and data sources as callable context
• Design reusable MCP tooling layers that agents can dynamically discover and invoke
• Ensure secure, governed access when agents interact with internal systems via MCP 

Tool & System Integration

• Integrate agents with enterprise tools including Jira, Salesforce, Slack, Notion, GitHub, ServiceNow, and others
• Build robust tool-calling interfaces, error handling, and retry logic for agent-tool interactions
• Connect agents to internal APIs, databases, and SaaS platforms via structured integration frameworks
• Develop and maintain an internal tool registry accessible by agents at runtime

Agentic Workflows & Automation

• Design end-to-end agentic workflows that automate complex, multi-step business processes
• Build automation pipelines that route tasks intelligently across agents, tools, and human reviewers
• Implement event-driven agent triggers integrated with enterprise systems and data streams
• Optimize workflows for reliability, observability, and cost efficiency

Slack Platform & Agent Accessibility

• Deploy and expose AI agents as interactive Slack bots and workflow apps
• Build Slack surfaces (slash commands, modals, home tabs, message actions) that connect users to agent capabilities
• Implement conversational agent interfaces in Slack with context retention across threads
• Enable agents to proactively surface insights, alerts, and task updates within Slack channels

RAG, Data & Knowledge Systems

• Implement RAG architectures to ground agents in enterprise knowledge bases
• Build vector pipelines, embeddings, and retrieval systems that agents query at runtime
• Integrate Snowflake (Cortex, Snowpark) as a data backbone for agent memory and structured data access

AI Security & Governance

• Implement prompt injection defenses, access controls, and data leakage prevention within agent pipelines
• Design RBAC and audit logging for all agent actions and tool invocations
• Ensure agents operate within defined security and compliance boundaries