Job Summary

We are seeking a hands-on AI Engineer with deep expertise in building AI Agents from the ground up, designing agentic workflows, and integrating enterprise tooling and MCP servers into production-grade agent systems. The ideal candidate has a builders mindset someone who has architected and shipped real agents, not just experimented with frameworks. This role sits at the intersection of AI engineering, workflow automation, and enterprise integration.

You will design agents that reason, act, and collaborate across business systems and make them accessible to end users through platforms like Slack

Responsibilities

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

What Youll Bring

- 5+ years of software or AI engineering experience





- Proven experience building AI Agents from the ground up - not just configuring frameworks

- Hands-on experience integrating MCP servers and clients into agent systems

- Experience designing and deploying agentic workflows and multi-step automations

- Experience building and deploying Slack bots or Slack-integrated AI applications

- Robust Python skills with experience in async, event-driven, and API-heavy architectures

- Experience with tool-calling patterns, function routing, and agent-tool error handling

- Solid understanding of LLM behavior, context windows, and prompt engineering

- Experience with RAG architectures and vector databases

Nice-to-Have

- Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel

- Experience with Snowflake Cortex or Snowpark for AI workloads

- Familiarity with OpenAI, Anthropic, Gemini, or open-source LLMs

- Experience with vector databases (Pinecone, Weaviate, Chroma, FAISS)

- Experience with Kubernetes, Docker, and cloud platforms (AWS, Azure, GCP)

- Experience with AI observability, tracing, and evaluation frameworks

- Snowflake certifications are a plus

Technical Skills

- Agentic AI (from-scratch agent design, orchestration, multi-agent systems)

- MCP (Model Context Protocol) - server and client development

- Tool integration and function calling

- Slack Platform (Bolt SDK, Block Kit, Slack APIs)

- Python (async, REST, event-driven)

- RAG Architecture Vector Databases

- Snowflake (Cortex, Snowpark, SQL)

- AI Security Governance

- Cloud Platforms (AWS / Azure / GCP)

- Git, CI/CD, DevOps

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Slack Platform & Agent Accessibility (Hyderabad)
🏢 Meltwater
📍 Hyderabad