Artificial Intelligence Engineer

About the Role

Engineers who have built and shipped production-grade, AI-native platforms on

AWS. You'll work at the intersection of scalable backend systems and applied AI —

designing agentic workflows, integrating LLMs, and owning cloud infrastructure end-

to-end.

Key Responsibilities

Design and build scalable backend services and APIs (REST/gRPC) for AI-

powered products

Architect and deploy cloud-native solutions on AWS (ECS/EKS, Lambda,

SQS, S3, RDS/Aurora, Bedrock)

Build and maintain LLM integration layers — prompt pipelines, RAG systems,

vector stores

Develop and deploy agentic workflows using frameworks like LangGraph,

CrewAI, AutoGen, or similar

Own MLOps/LLMOps pipelines — model serving, evaluation, and

observability

Collaborate with product and ML teams to productionize AI features

Must-Have

6–8 years of backend engineering experience (Python and/or Node.js/Go)

Strong hands-on AWS experience — Serverless, container orchestration

Production experience with LLMs (OpenAI, Anthropic, Bedrock, or open-

source models)

Hands-on with at least one agentic framework (LangGraph, CrewAI,

AutoGen, Semantic Kernel, etc.)

Experience with vector databases (Pinecone, Weaviate, pgvector, or similar)

Solid understanding of RAG architecture, embeddings, and prompt

engineering

Good to Have

Model fine-tuning experience — SFT, LoRA/QLoRA on open-source models

(Llama, Mistral, etc.) using Hugging Face Transformers.

Fine-tuning pipelines on AWS (SageMaker Training Jobs, EC2 GPU, S3 data

management)

Familiarity with RLHF, or instruction tuning workflows

Multi-agent orchestration and tool use patterns

AI evaluation frameworks (RAGAS, DeepEval) and guardrails implementation

Prior experience building internal AI platforms or developer tooling