Senior AI Engineer – Agentic AI & LLM Platforms
We are looking for a hands-on Senior AI Engineer with strong software engineering experience building production applications using Generative AI, frontier LLMs, and agentic AI architectures.
The role will focus on designing and developing multi-agent systems, reusable AI tools and platforms, enterprise integrations, and AI solutions using AWS Bedrock. We are also looking for engineers who actively use AI-assisted development tools as part of their day-to-day engineering workflow.
Experience
- 5+ years of overall software engineering experience, preferably across backend, cloud, or distributed applications.
- 2+ years of hands-on experience with Generative AI, LLM applications, and agentic AI frameworks.
- Experience taking AI/LLM applications from prototype through production.
Key Responsibilities
- Design, develop, and deploy agentic and multi-agent AI systems for production use cases.
- Build agent workflows involving reasoning, planning, routing, delegation, tool execution, memory, state management, checkpointing, parallel execution, and human-in-the-loop patterns.
- Work with frontier LLMs and make practical decisions around model selection, prompting, structured outputs, context management, latency, token usage, quality, and cost.
- Build and integrate MCP servers, reusable tools, and function/tool-calling workflows connecting agents with APIs, databases, enterprise systems, and internal applications.
- Develop and orchestrate production-grade agent workflows and multi-agent systems.
- Build AI applications using AWS Bedrock, including foundation models, Knowledge Bases/RAG, Guardrails, and related services.
- Build RAG and enterprise retrieval pipelines, including ingestion, embeddings, vector search, reranking, metadata filtering, and grounding.
- Design multi-model architectures, selecting models based on task complexity, quality, latency, and cost.
- Implement LLM observability and evaluation, including tracing, tool execution, response quality, grounding, hallucination, latency, token consumption, and task completion.
- Implement AI security patterns including guardrails, prompt-injection defenses, tool permissioning, credential management, and safe agent execution.
- Build reusable agent platforms and frameworks, not just standalone chatbot applications.
- Use AI coding assistants and coding agents effectively for development, debugging, testing, refactoring, and code analysis while maintaining ownership of code quality and architecture.
- Work with product and engineering teams to take AI use cases from POC through production.
Required Skills
- Strong Python development skills with experience building production APIs and backend services.
- Strong hands-on experience with Generative AI, LLMs, agentic architectures, and multi-agent orchestration.
- Experience with frontier models such as OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, or equivalent.
- Hands-on experience with MCP, tool/function calling, structured outputs, and agent-to-tool integration.
- Experience with AWS Bedrock and building AI/LLM applications on AWS.
- Experience with one or more agentic AI frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent.
- Strong understanding of RAG, embeddings, vector databases, semantic search, and reranking.
- Experience with LLM observability, tracing, evaluation, and production monitoring using tools like Langsmith or equivalent.
- Working knowledge of AWS services such as Lambda, ECS/EKS, API Gateway, S3, DynamoDB/RDS, OpenSearch, SQS/SNS, and CloudWatch.
- Comfortable with AI-assisted software development, using tools such as GitHub Copilot, Cursor, Claude Code, Codex, or similar coding agents.
What We Are Looking For
This is not a traditional data science or model-training role. We are looking for an engineer who knows how to use modern foundation models to build real software systems.
The right candidate should be comfortable moving between an LLM, agent workflow, MCP tool, Python service, RAG pipeline, and AWS Bedrock, and understand how these pieces come together in a reliable production system. They should also be AI-native in how they develop software, using AI coding tools extensively while retaining ownership of architecture, code quality, testing, security, and the final implementation.
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