Senior Machine Learning Engineer

Key Prediction Scientific Solutions Inc.
Austin, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
$ 190K

Job location

Austin, United States of America

Tech stack

API
Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Computer Vision
Azure
Cloud Computing
Continuous Integration
Python
Machine Learning
NoSQL
NumPy
Object Detection
OpenCV
Scrum
Recommender Systems
TensorFlow
Standard Sql
Software Engineering
Data Processing
Google Cloud Platform
Cloud Platform System
Chatbots
PyTorch
Flask
Large Language Models
Prompt Engineering
Generative AI
GIT
FastAPI
Pandas
Scikit Learn
Kubernetes
Information Technology
Machine Learning Operations
Api Design
GPT
Software Version Control
Data Pipelines
Unsupervised Learning
Microservices

Job description

We are seeking a highly skilled Senior Machine Learning Engineer to join our growing AI team. In this role, you will design, develop, deploy, and maintain Machine Learning and Artificial Intelligence solutions that drive business value through intelligent automation, predictive analytics, and innovative AI-powered products.

You will work closely with Data Scientists, Data Engineers, Software Engineers, and business stakeholders to transform experimental models into scalable, production-ready solutions.

Key Responsibilities

  • Design, train, optimize, and deploy Machine Learning models in production environments.
  • Develop Computer Vision solutions for image and video detection, classification, and segmentation.
  • Build and maintain scalable data pipelines for model training and inference.
  • Integrate Machine Learning models through APIs and microservices.
  • Select, evaluate, and adapt Large Language Models (LLMs) for business use cases.
  • Implement Prompt Engineering, Retrieval-Augmented Generation (RAG), and fine-tuning techniques.
  • Develop Generative AI solutions, including intelligent assistants, automated summarization, and recommendation systems.
  • Monitor model performance, drift, and retraining processes.
  • Optimize cloud infrastructure costs and model performance across AWS, Azure, or GCP environments.
  • Collaborate within Agile/Scrum teams to deliver high-quality AI solutions.
  • Ensure best practices in software development, scalability, maintainability, and documentation.
  • Partner with cross-functional teams to translate business requirements into AI-driven solutions.

Requirements

Do you have a valid AWS Certified Machine Learning - Specialty certification?, Do you have experience in Production systems?, Do you have a Bachelor's degree?, * Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.

  • Master's degree or postgraduate specialization in Machine Learning, Artificial Intelligence, or related disciplines is highly preferred.

Professional Experience

  • 5+ years of proven experience as a Machine Learning Engineer.
  • Hands-on experience developing and deploying Machine Learning solutions in production environments.
  • Strong background in Computer Vision projects.
  • Experience building scalable AI systems in cloud environments.
  • Experience working in Agile/Scrum environments.

Technical Skills

Machine Learning & Data Science

  • Advanced proficiency in Python.
  • Strong experience with NumPy, Pandas, and related data processing libraries.
  • Expertise in supervised and unsupervised learning techniques.
  • Experience with TensorFlow, PyTorch, and Scikit-learn.
  • Strong understanding of model training, evaluation, optimization, and deployment.

Computer Vision

  • Experience with OpenCV, Pillow, Albumentations, or similar frameworks.
  • Knowledge of image classification, object detection, segmentation, and video analytics.

Software Engineering

  • Experience developing APIs using FastAPI and/or Flask.
  • Proficiency with Git and version control best practices.
  • Strong knowledge of SQL and NoSQL databases.
  • Experience building scalable microservices architectures.

Cloud & MLOps

  • Experience deploying Machine Learning solutions on AWS, Azure, or Google Cloud Platform.
  • Familiarity with MLflow, Kubeflow, Airflow, Vertex AI Pipelines, and/or AWS SageMaker.
  • Understanding of CI/CD practices for Machine Learning systems.

Generative AI & LLMs

  • Hands-on experience with LLMs such as GPT, Llama, Mistral, Claude, Falcon, or Gemini.
  • Experience with Prompt Engineering and Retrieval-Augmented Generation (RAG).
  • Knowledge of fine-tuning techniques and model optimization strategies.
  • Familiarity with LoRA, quantization, distillation, and other cost-optimization approaches.
  • Experience building AI-powered assistants, chatbots, and multimodal applications.

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