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Fractal Analytics

Enterprise AI solutions for decision making

Lead Architect

Applied Machine LearningUpdated on Nov 13
Full-timeNo salary listedIn-person

About the Job

Requirements

  • 10–14 years of experience working on machine learning projects, including product-building, hands-on technical work, technical leadership, and leading development teams.
  • Experience developing, training, deploying, and monitoring models at scale for production use cases.
  • Strong knowledge of Python, data engineering, FastAPI, and natural language processing.
  • Knowledge of LangChain, LlamaIndex, LangTrace, Langfuse, large language model evaluation, MLflow, and BentoML.
  • Experience working with proprietary and open-source large language models.
  • Experience with large language model fine-tuning, including parameter-efficient fine-tuning and continued pretraining.
  • Experience creating agentic artificial intelligence workflows using frameworks such as CrewAI, LangGraph, AutoGen, and Semantic Kernel.
  • Experience in performance optimization, retrieval-augmented generation, guardrails, artificial intelligence governance, prompt engineering, evaluation, and observability.
  • Experience deploying generative artificial intelligence applications at scale in cloud and on-premises environments for production using DevOps practices.
  • Experience in DevOps and MLOps.
  • Working knowledge of Kubernetes and Terraform.
  • Experience with at least one cloud platform—AWS, Google Cloud Platform, or Azure—for deploying artificial intelligence services.
  • Product thinking encompassing ideation, prototyping, and scaling internal accelerators for LLMOps.
  • Technical skills with LLMOps frameworks including LangChain, MLflow, BentoML, Ray, Truss, and FastAPI.
  • Experience with prompt evaluation and scoring systems including OpenAI Evals, Ragas, Rebuff, and Outlines.
  • Cloud-native deployment skills with Kubernetes, Helm, Terraform, Docker, and GitOps.
  • Machine learning pipeline skills with Airflow, Prefect, Feast, and feature stores.
  • Data stack skills with Spark or Flink, Parquet or Delta, and lakehouse patterns.
  • Cloud platform skills with Azure Machine Learning, Google Cloud Vertex AI, and AWS Bedrock or SageMaker.
  • Python is required; Bash, YAML, and Terraform HCL are preferred.

Responsibilities

  • Architect and build scalable LLMOps platforms for enterprise-grade generative artificial intelligence systems.
  • Design and manage end-to-end large language model pipelines from data ingestion and embedding through evaluation and inference.
  • Drive large language model infrastructure, including memory management, token control, prompt chaining, and context optimization.
  • Lead scalable large language model deployment frameworks using Kubernetes and GPU-aware scaling.
  • Build agentic artificial intelligence operations capabilities, including agent evaluation, observability, orchestration, and reflection loops.
  • Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response.
  • Drive end-to-end platform automation using Docker, Kubernetes, GitOps, DevOps, and Terraform.
  • Productionize large language model-powered applications using modular, reusable, and secure patterns.
  • Create evaluation pipelines, including prompt orchestration, feedback loops, and fine-tuning workflows.
  • Design systems for model and prompt versioning, artificial intelligence governance, automated testing, and prompt quality scoring.
  • Architect cloud-native and hybrid deployment strategies for large-scale inference.

Desired Qualifications

  • Bash, YAML, and Terraform HCL are preferred.

About the Company

Fractal Analytics

Enterprise AI solutions for decision making

Fractal Analytics provides AI-powered analytics and decision-support solutions for large enterprises, including Fortune 500 companies. Its products combine data science, machine learning, and natural language processing to extract insights, automate tasks, and enable conversations: Crux Intelligence (BI), Senseforth.ai (conversational AI), Eugenie.ai (sustainability), and Asper.ai (revenue growth). The company differentiates itself with a broad, enterprise-focused product portfolio and a global delivery footprint of 4,000+ employees across 16 locations, plus industry analyst recognition and workplace awards. Its goal is to power every human decision in the enterprise by bringing AI, engineering, and design together to help leading companies make better decisions.

Company size
5,001-10,000
Company stage
IPO
Headquarters
New York City, New York
Founded
2000

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