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ML Engineer

AgreeYa Solutions, Inc.
Plano, TX, United States
24 days ago
Apply on www.dice.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Application Programming Interfaces (APIs) Microsoft Azure Python (Programming Language) PostgreSQL Machine Learning MongoDB NoSQL Redis Software Engineering SQL Databases Workflow Management Systems Snowflake
+8 more
Apache Spark Data Lakes Pyspark Apache Kafka Machine Learning Operations Kibana Data Pipelines Databricks

Requirements

This role is ML Engineering with hands-on Software Engineering skills using Python and Pyspark, Databricks. Strong Python, Databricks and PySpark experience with Spark, Kafka, Snowflake, MongoDB, PostgreSQL, Redis, Azure cloud. Advanced No-SQL (MongoDB) and SQL (Postgres) and data modeling for slowly changing dimensions / temporal mappings. Experience building production-grade ML pipelines, and data pipelines. Solid Python development skills with APIs development (FastAPIs) and familiarity with ML workflows. Delta Lake, MLflow, and workflow orchestration experience. Agentic development and GenAI experience will be a big plus Familiarity with ELK (Elastic Kibana) The candidate must have classical ML/DS exposure with production pipeline design and development experience.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:37 min

Comparing traditional SQL tables versus NoSQL non-tabular databases

Stanimira Vlaeva · JS Congress

3:55 min

Demonstrating semantic routing thresholds with the Redis vector library

3:21 min

Deploying a primary Elasticsearch and Kibana cluster configuration

Philipp Krenn · World Congress 2022

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

4:51 min

Executing simple full-text search queries using the Kibana interface

Derek Binkley · LIVE

3:42 min

Comparing in-memory and Redis storage for cache scalability

Simone Sanfratello · World Congress 2022

Videos

See all
Leveraging Real time data in FSIs thumbnail
41:51
LIVE • almost 3 years ago

Leveraging Real time data in FSIs

Tim Faulkes

Introduction to Azure Machine Learning thumbnail
54:27
LIVE • over 4 years ago

Introduction to Azure Machine Learning

Jose Luis Latorre Millas

Debug a Kubernetes Operator thumbnail
31:52
World Congress 2022 • about 4 years ago

Debug a Kubernetes Operator

Philipp Krenn

Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis) thumbnail
34:39
about 1 year ago

Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)

Add Location-based Searching to Site with ElasticSearch thumbnail
45:13
LIVE • almost 6 years ago

Add Location-based Searching to Site with ElasticSearch

Derek Binkley

Accelerating Authentication Architecture: Taking Passwordless to the Next Level thumbnail
49:52
LIVE • almost 3 years ago

Accelerating Authentication Architecture: Taking Passwordless to the Next Level

Yedidya Schwartz

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