Fractal Labs

5 days ago

MLOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Build and productionize a portfolio of machine learning solutions in purchase and underwriting by designing and operating the inference layer, data and feature pipelines, and batch/real-time parity. You’ll implement FastAPI model-serving services (sync/async), containerize and deploy them, and use Databricks/PySpark to create reproducible pipelines. Focus on reliability and observability, incident diagnosis, and strong engineering practices with CI/CD, testing, and clear documentation for internal ownership.

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Design and build FastAPI model-serving services with robust request/response contracts, authentication/authorization, validation, logging, tracing, and metrics.
•Implement queue-based asynchronous serving with producers/consumers, retries, idempotency, back-pressure, dead-letter handling, and end-to-end request traceability.
•Own data preprocessing, transformation, and feature engineering code; refactor notebook/script logic into modular, tested, reusable components.
•Build reproducible training and batch inference pipelines on Databricks and PySpark, including model artifact/version management and promotion across environments.
•Ensure parity and reliability across the ML lifecycle by aligning feature definitions across training/batch/real-time and implementing monitoring, drift/data-quality checks, and production incident fixes.

Pay and Benefits

Salary: USD 120,000 - 140,000 annually

Perks:Health InsuranceDentalVisionLife InsuranceDisability401kPaid HolidaysParental Leave

Key Requirements

•Deep hands-on Python for data engineering and application development, with comfort across SQL, PySpark, and shell scripting.
•Production experience building services with FastAPI (or comparable Python web framework), including auth, validation, error handling, and observability.
•Experience with queue-based asynchronous processing systems (Kafka, RabbitMQ, SQS, Redis Streams, Celery, or equivalent) and operational concerns like retries, idempotency, back-pressure, and dead-letter queues.
•Strong Docker and containerization skills; comfortable with Kubernetes concepts.
•Hands-on Databricks experience with MLFlow and distributed compute in Spark, plus familiarity with common ML libraries (scikit-learn, XGBoost, PyTorch or similar).

Skills:CommunicationCollaborationDocumentationIncident diagnosis

Tech Stack:PythonSQLPySparkFastAPIDockerKubernetesDatabricksMLFlowSparkScikit-learnXGBoostPyTorchKafkaRabbitMQSQSRedis StreamsCeleryJenkinsGitHub ActionsAirflow

Company Brief

Fractal Labs

Provides AI-driven analytics, decision intelligence, and engineering solutions to Fortune 500 companies, helping enterprises apply advanced analytics and machine learning for marketing, supply chain, finance, and customer experience transformation.

Industry: Consulting

Company Size: Enterprise (1,001+ employees)

Growth: Established Company

Headquarters: New York, United States

Founded: 2000

Website LinkedIn

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Fractal Labs

5 days ago

MLOps Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Mid level

Key Responsibilities

•Design and build FastAPI model-serving services with robust request/response contracts, authentication/authorization, validation, logging, tracing, and metrics.
•Implement queue-based asynchronous serving with producers/consumers, retries, idempotency, back-pressure, dead-letter handling, and end-to-end request traceability.
•Own data preprocessing, transformation, and feature engineering code; refactor notebook/script logic into modular, tested, reusable components.
•Build reproducible training and batch inference pipelines on Databricks and PySpark, including model artifact/version management and promotion across environments.
•Ensure parity and reliability across the ML lifecycle by aligning feature definitions across training/batch/real-time and implementing monitoring, drift/data-quality checks, and production incident fixes.

Pay and Benefits

Salary: USD 120,000 - 140,000 annually

Perks:Health InsuranceDentalVisionLife InsuranceDisability401kPaid HolidaysParental Leave

Key Requirements

•Deep hands-on Python for data engineering and application development, with comfort across SQL, PySpark, and shell scripting.
•Production experience building services with FastAPI (or comparable Python web framework), including auth, validation, error handling, and observability.
•Experience with queue-based asynchronous processing systems (Kafka, RabbitMQ, SQS, Redis Streams, Celery, or equivalent) and operational concerns like retries, idempotency, back-pressure, and dead-letter queues.
•Strong Docker and containerization skills; comfortable with Kubernetes concepts.
•Hands-on Databricks experience with MLFlow and distributed compute in Spark, plus familiarity with common ML libraries (scikit-learn, XGBoost, PyTorch or similar).

Skills:CommunicationCollaborationDocumentationIncident diagnosis

Tech Stack:PythonSQLPySparkFastAPIDockerKubernetesDatabricksMLFlowSparkScikit-learnXGBoostPyTorchKafkaRabbitMQSQSRedis StreamsCeleryJenkinsGitHub ActionsAirflow

Company Brief

Fractal Labs

Industry: Consulting

Company Size: Enterprise (1,001+ employees)

Growth: Established Company

Headquarters: New York, United States

Founded: 2000

Website LinkedIn