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Fractal

NFT marketplace for blockchain gaming assets

Senior Generative AI Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeCambridge, MA, USA

About the Job

Requirements

  • Requires 8–10 years of software engineering experience, including at least 3 years in Generative AI or AI solution development.
  • Requires hands-on experience building production-grade Generative AI solutions.
  • Requires expertise in Agentic AI, AI agents, multi-agent architectures, tool calling, function calling, orchestration, and Model Context Protocol (MCP).
  • Requires experience designing and implementing Retrieval-Augmented Generation (RAG) architectures, including embeddings, vector databases, retrieval pipelines, reranking, and semantic search.
  • Requires hands-on experience with Amazon Bedrock, foundation model integration, inference optimization, prompt engineering, and model orchestration.
  • Requires deep understanding of large language models, prompt engineering, evaluation, model selection, and deployment patterns.
  • Requires strong proficiency in Python for backend development, AI application development, and API implementation.
  • Requires experience with AWS cloud services, including Bedrock, Lambda, ECS/EKS, API Gateway, S3, IAM, CloudWatch, DynamoDB, OpenSearch, and Step Functions.
  • Requires experience with Terraform and Infrastructure as Code for cloud automation and platform deployment.
  • Requires experience building cloud-native applications using APIs, microservices, Docker, Kubernetes, and CI/CD pipelines.
  • Requires understanding of AI observability, monitoring, tracing, and telemetry.
  • Requires knowledge of relational, NoSQL, vector, and graph databases.
  • Requires experience with security, governance, compliance, and responsible AI frameworks.

Responsibilities

  • Design, develop, and maintain scalable, secure, and highly available Generative AI platforms and AI-powered applications on AWS.
  • Architect and implement Agentic AI solutions, multi-agent systems, tool integrations, function calling, and workflow orchestration frameworks.
  • Design and implement enterprise-grade RAG solutions using embeddings, vector databases, semantic search, reranking, and knowledge retrieval techniques.
  • Build AI applications leveraging Amazon Bedrock for large language model inference, model orchestration, prompt management, and foundation model integrations.
  • Develop reusable AI platform components, software development kits, APIs, accelerators, and reference architectures.
  • Build knowledge-driven AI systems using vector stores, graph databases, and knowledge graph technologies.
  • Create evaluation and benchmarking frameworks to measure AI quality, accuracy, latency, safety, and performance.
  • Implement AI governance, security controls, responsible AI practices, guardrails, and compliance mechanisms.
  • Develop observability capabilities including telemetry, monitoring, tracing, logging, and operational dashboards.
  • Design and develop cloud-native microservices and APIs using Python and modern software engineering practices.
  • Automate cloud infrastructure provisioning and deployment using Terraform and Infrastructure as Code principles.
  • Build and maintain CI/CD pipelines for AI and cloud workloads.
  • Collaborate with cross-functional engineering, security, product, and business teams to drive AI platform adoption and best practices.
  • Mentor junior engineers and contribute to architecture reviews and technical leadership initiatives.

Desired Qualifications

  • Experience with LangChain, LangGraph, or Semantic Kernel.
  • Experience with LlamaIndex.
  • Experience with the OpenAI Agents SDK or similar agent frameworks.
  • Experience with Amazon OpenSearch, Pinecone, Weaviate, Chroma, FAISS, or similar vector database technologies.
  • Experience with OpenTelemetry, Langfuse, Arize Phoenix, or MLflow.
  • Experience with Grafana, Prometheus, or CloudWatch Observability.
  • Experience with Neo4j and knowledge graph technologies.
  • Experience with distributed systems and scalable platform engineering.
  • Experience with MLOps, LLMOps, and AI deployment pipelines.
  • Exposure to fine-tuning, model evaluation, and AI benchmarking frameworks.

About the Company

Fractal

NFT marketplace for blockchain gaming assets

Fractal.is operates a blockchain-based marketplace for gaming NFTs where developers can list games and in-game assets and players can buy, sell, and trade unique digital items. The platform supports minting and secondary sales of NFTs, with transactions generating revenue through commissions. It emphasizes community engagement through channels like Discord and Twitter to drive listing activity and user retention. Compared to others, Fractal.is specializes in the gaming sector, aggregating multiple games and assets under one marketplace, and focuses on building a connected metaverse ecosystem for developers and gamers. Its goal is to grow a active, trusted marketplace that enables true ownership of digital assets and vibrant participation in the gaming/NFT space.

Company size
1-10
Company stage
Seed
Total funding
$35.2M
Headquarters
San Francisco, CA
Founded
2021

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