AI/ML Software Engineer

Dfuse Technologies, Inc.; Ai (OH); On-site; USD 110,000 - 160,000; Full time

Job summary

Dfuse Technologies, Inc. seeks an AI/ML Software Engineer to bridge research and production, designing, building, testing, and deploying scalable AI features and robust ML architectures in collaboration with product managers, data scientists, and backend engineers.

You will drive model integration, data pipelines, deployment, monitoring, and cross-functional collaboration, delivering high-quality APIs, automated tests, and measurable performance improvements.

Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, AI, ML, Data Science, or related field.
• Experience: 2+ years building and shipping ML models in production environments.
• Programming: Proficiency in Python with NumPy and Pandas.

Responsibilities

• Design, fine-tune, and evaluate ML models and integrate them into production apps via robust APIs.
• Build, optimize, and maintain data pipelines for preprocessing, feature engineering, and continual training.
• Containerize and deploy models using modern cloud infrastructure with an emphasis on scalability and uptime.
• Monitor deployed models for drift, resource use, and inference costs; optimize performance.
• Collaborate with product and engineering to scope AI use cases and define success metrics.
• Establish automated tests and evaluation frameworks for model accuracy, robustness, and safety.

Skills

Python

NumPy

Pandas

RESTful APIs

Git

Data structures

Algorithms

ML frameworks

PyTorch

TensorFlow

Scikit-Learn

SQL

CI/CD

Docker

Kubernetes

MLflow

LangSmith

Prometheus

Education

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field

Tools

PyTorch

TensorFlow

Scikit-Learn

Docker

Kubernetes

MLflow

LangSmith

Prometheus

Job description

We are seeking a talented and driven AI/ML Software Engineer to bridge the gap between advanced machine learning research and high-performance software production systems. In this role, you will design, build, test, and deploy scalable AI-driven features and robust machine learning architectures, working closely with product managers, data scientists, and backend engineering teams.

Key Responsibilities

Model Integration & Development: Design, fine-tune, and evaluate machine learning models, integrating them seamlessly into production applications via robust APIs.

Data Pipeline Engineering: Build, optimize, and maintain efficient data pipelines for preprocessing, feature engineering, and continuous model training.

Production Deployment & MLOps: Containerize and deploy models using modern cloud infrastructure, ensuring low latency, high scalability, and uptime.

Monitoring & Performance Tuning: Track deployed models for performance drift, resource consumption, accuracy gaps, and inference cost efficiency.

Cross-Functional Collaboration: Partner with product and engineering stakeholders to scope AI use cases, translate business requirements into technical specs, and define success metrics.

Quality & Evaluation: Establish automated test suites and rigorous evaluation frameworks to measure model accuracy, robustness, and safety.

Minimum Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.

• Experience: 2+ years of professional software engineering experience, including hands-on work building, shipping, and supporting machine learning models in production environments.

Programming Proficiency: Strong software engineering fundamentals with expert-level proficiency in Python (including standard scientific computing libraries like NumPy and Pandas).

ML Ecosystem: Working knowledge of major machine learning frameworks such as PyTorch , TensorFlow , or Scikit-Learn .

Software Core Concepts: Solid foundation in data structures, algorithms, system design, RESTful APIs, and Git version control workflows.

Data Management: Practical experience with SQL and relational or non-relational database management.

Preferred Qualifications

Advanced Degree: Master’s or Ph.D. degree specializing in Machine Learning, Natural Language Processing (NLP), or Computer Vision.

Cloud Platforms: Hands-on experience deploying and managing workloads on AWS , Microsoft Azure , or Google Cloud Platform (GCP) .

Generative AI & LLMs: Experience working with Large Language Models, Retrieval-Augmented Generation (RAG) systems, vector databases, and modern AI application frameworks.

Infrastructure & Automation: Proven familiarity with Docker , Kubernetes , CI/CD pipeline automation, and container orchestration.

Observability: Exposure to model monitoring and evaluation tooling (e.g., MLflow, LangSmith, or Prometheus) to track production health and trace model outputs.