AI Engineer: End-to-End ML & MLOps Architect

nexacode; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

nexacode is seeking an AI/ML Engineer to design, build, and deploy high-performance models and AI-driven applications. You will own end-to-end development from data engineering to production deployment and MLOps tracking.

Responsibilities include training predictive models, building data pipelines and feature stores, deploying REST APIs on cloud platforms, and collaborating with backend teams to integrate AI endpoints into core products.

Qualifications

• 4+ years of professional software engineering experience with mastery in Python.
• Hands-on experience with PyTorch or TensorFlow.
• Experience deploying models on AWS SageMaker, Azure ML, or GCP with CI/CD automation tools.
• Strong API/microservices development experience (FastAPI, Flask) for model inference.
• Expert SQL skills and experience with large-scale relational/non-relational data infrastructure.

Responsibilities

• Architect, train, and evaluate predictive models, NLP algorithms, or generative AI workflows to solve complex enterprise problems.
• Build, scale, and maintain automated data preprocessing pipelines and feature stores.
• Deploy machine learning models as scalable REST APIs into cloud production environments.
• Monitor model performance, track feature drift, and implement continuous retraining mechanisms.
• Collaborate with backend engineers and data teams to seamlessly integrate AI endpoints into core software products.

Skills

Python

RESTful services

SQL

CI/CD automation

MLOps

Collaborative programming

Tools

PyTorch

TensorFlow

AWS SageMaker

Azure ML

GCP

FastAPI

Flask

Pinecone

Weaviate

Milvus

LangChain

Job description

nexacode is seeking an AI/ML Engineer to design, build, and deploy high-performance models and AI-driven applications. You will own end-to-end development from data engineering to production deployment and MLOps tracking.

Responsibilities include training predictive models, building data pipelines and feature stores, deploying REST APIs on cloud platforms, and collaborating with backend teams to integrate AI endpoints into core products.