Machine Learning Engineer

Remote • Posted 2 hours ago • Updated 2 hours ago

Full Time

No Travel Required

Remote

135000 - 155000/yr

Fitment

Dice Job Match Score™

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Job Details

Skills

Machine Learning
ML
LLMs
RAGs
AI agent
CI/CD workflows
MLflow
SageMaker
or custom APIs
S3
EC2
Lambda

Summary

Required Skills:

5+ years of experience in ML Engineering or Applied Machine Learning.
Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
Proficient with Databricks, MLflow, and PySpark.
Solid understanding of model lifecycle and MLOps practices.
Experience with AWS-based data infrastructure and related DevOps practices.
Demonstrated ability to productionize models and integrate with business system
Strong understanding of mathematics and statistics relevant to machine learning and AI.
Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.).
Solid background in software engineering principles and best practices.
Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.).
Practical experience with LLMs, RAGs, and AI agent architectures.
Proficiency with the Databricks platform for data engineering and ML pipelines.
Advanced programming skills in Python.
Excellent communication and teamwork abilities.

Preferred Skills:

Experience building and deploying interactive UIs for AI models using Streamlit, Gradio, or similar frameworks for rapid prototyping and real-time model interactions
Business acumen and ability to align AI solutions with organizational goals.
Optimize compute and storage resources for performance and cost-efficiency.

Role and Responsibilities

Model Development

Collaborate with data scientists and SMEs to develop ML models using curated datasets.
Conduct experiments, prototypes, and proof-of-concepts to validate model performance.
Create scalable and reusable training pipelines using Databricks notebooks and MLflow.

Implementation and Optimisation

LLMs (Large Language Models), RAGs, and AI agent systems for various business applications. Deployment & MLOps
Operationalize models with robust CI/CD workflows.
Deploy models usingMLflow, SageMaker, or custom APIs.
Monitor production models for accuracy, drift, and latency; manage retraining schedules.

Data Integration & Architecture Alignment

Work closely with Data Engineering to align ML pipelines with the Bronze, Silver, Gold layers of a Medallion Architecture.
Engineer high-quality features and maintain training/inference pipelines.

Cloud and Platform Engineering

Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions.

Collaboration & Documentation

Document ML artifacts, processes, and performance outcomes.
Contribute to agile project ceremonies and maintain a feedback loop with stakeholders.
Share knowledge and mentor junior team members.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91052859
Position Id: HALDOTREM30115
Posted 2 hours ago

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Machine Learning Engineer

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Job Details

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