ML/AI Engineer

Overview

On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
No Travel Required

Skills

Configuration Management
Artificial Intelligence
Big Data
Cloud Computing
Deep Learning
Collaboration
Continuous Delivery
Google Cloud Platform
Machine Learning (ML)
Continuous Integration
Data Science
Good Clinical Practice
Orchestration
Kubernetes
Management
Mentorship
Microsoft Azure
Semantic Search
Onboarding
Performance Tuning
Amazon Web Services
Apache Hadoop
Apache Hive
Apache Spark
TensorFlow
Terraform
Testing
Vector Databases
Docker
Neo4j
PyTorch
Python
Research
Roadmaps
Scripting
Vertex
Workflow
Algorithms
Writing
XGBoost
scikit-learn

Job Details

Job Description:

  • Design and implement machine learning models and pipelines for real-world applications.
  • Build and maintain robust ML pipelines using tools like Vertex AI Pipelines and Terraform for infrastructure-as-code.
  • Develop CI/CD templates and configuration management for ML workflows.
  • Implement onboarding and monitoring processes for deployed ML models.
  • Integrate and manage knowledge graphs and vector databases to support semantic search and retrieval-augmented generation (RAG) systems.
  • Collaborate with cross-functional teams to translate business problems into ML solutions.
  • Develop and maintain scalable data pipelines and model serving infrastructure.
  • Conduct rigorous testing, validation, and performance tuning of models.
  • Contribute to architecture design, code reviews, and technical roadmaps.
  • Stay current with the latest ML research and tools and apply them pragmatically.
  • Mentor junior engineers and promote best practices in ML engineering.

Mandatory Skills Bachelor's or master's degree in computer science, Engineering, Mathematics, or a related field. PhD is a plus. 5+ years of experience in machine learning, data science, or AI engineering. Proficient in Python and libraries like Scikit-learn, TensorFlow, PyTorch, XGBoost, etc. Strong understanding of machine learning algorithms, deep learning architectures, and statistical methods. Hands-on experience building and deploying ML models in cloud environments (Google Cloud Platform, AWS, or Azure). Experience with containerization tools (Docker, Kubernetes) and ML workflow tools (MLflow, TFX). Familiarity with big data technologies such as Spark, Hive, or Hadoop. Experience with Google Vertex AI and writing Terraform scripts for infrastructure automation. Experience with CI/CD pipelines, configuration management, and onboarding/monitoring of ML systems. Experience working with knowledge graphs and vector databases (e.g., Neo4j, Weaviate, Pinecone, FAISS). Strong understanding of data and AI pipeline configuration and orchestration.

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