Software Engineer

Jobtailor; Arizona; On-site; USD 110,000 - 160,000; Full time

Job summary

Jobtailor seeks an AI/ML Software Engineer in the United States (Arizona) to design, develop, and optimize software tools for engineering workflows. You will apply end-to-end AI/ML solutions, build intelligent features, and collaborate with cross-functional teams to deliver scalable systems.

The role requires a Master’s degree in AI/ML or related fields and hands-on experience with ML frameworks, Docker/Kubernetes, and cloud platforms.

Qualifications

• Master’s degree in AI/ML, Data Science, CS, SWE, or Electrical Engineering.
• 6 months of experience building engineering applications or automation tools.
• Hands-on AI/ML experience and practical software deployment.
• Strong programming in Python/C++/Java.
• Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience deploying AI/ML services with Docker/Kubernetes and cloud platforms.
• MLOps practices: versioning, experiments, monitoring, evaluation.
• Knowledge of systems design, data pipelines, and big data tools.

Responsibilities

• Design, develop, and enhance software tools and automation for engineering workflows.
• Apply AI/ML end-to-end to improve tool capability and workflow efficiency.
• Build and integrate intelligent features, including data-driven automation and predictive capabilities.
• Collaborate with engineering, research, and product teams to translate requirements into scalable solutions.
• Contribute to architecture, design, implementation, testing, and deployment of AI/ML tools.
• Develop robust, production-quality code in a fast-paced environment.
• Evaluate new AI/ML technologies and approaches to drive innovation.
• Support experimentation, prototyping, and iterative refinement in a research-focused setting.
• Ensure software solutions are reliable, scalable, and aligned with goals.
• Participate in code reviews and cross-functional problem solving.

Skills

Python

C++

Java

AI/ML tech

MLOps

Docker

Kubernetes

REST APIs

Distributed systems

Education

Master’s degree in AI/ML or related

Tools

PyTorch

TensorFlow

scikit-learn

Spark

Databricks

Kafka

LLMOps tooling

Job description

Responsibilities

• Design, develop, and enhance software tools and automation solutions that support engineering workflows in advanced packaging and silicon enablement
• Apply AI/ML techniques end-to-end to improve tool capability, automation, decision support, and workflow efficiency
• Build and integrate intelligent features into software systems, including data-driven automation, predictive capabilities, and workflow optimization
• Collaborate closely with engineering, research, and product teams to understand requirements and translate them into scalable technical solutions
• Contribute to the architecture, design, implementation, testing, and deployment of AI/ML-enabled software tools
• Develop robust, maintainable, and production-quality code in a fast-paced development environment
• Evaluate new AI/ML technologies, frameworks, and approaches to drive innovation in engineering productivity and automation
• Support experimentation, prototyping, and iterative refinement of new solutions in a research-focused setting
• Ensure software solutions are reliable, scalable, and aligned with engineering and business goals
• Participate in code reviews, design discussions, and cross-functional technical problem-solving

Requirements

• Master’s degree in AI/ML, Data Science, Computer science, Software Engineering, Electrical Engineering
• 6 months of experience in software development skills with experience in building engineering applications, automation tools, or enterprise software solutions
• Hands‑on experience with AI/ML technologies and their practical application in software systems
• Programming languages such as Python, C++, Java, or similar
• Software design, development, testing, and debugging in a collaborative environment
• Hands‑on experience designing, deploying, and maintaining scalable, reliable AI/ML production systems
• End‑to‑end experience with generative AI and LLM‑based solutions, including prompt engineering, embeddings, vector databases, semantic search, and RAG workflows
• Foundations in data structures, algorithms, and software engineering principles
• Hands‑on experience building scalable applications, backend services, APIs, or distributed systems, including integration of ML models into software
• Experience with machine learning systems, including model training, evaluation, inference, and use of frameworks such as PyTorch, TensorFlow, or scikit‑learn
• Experience deploying AI/ML services using Docker, Kubernetes, cloud platforms, REST/gRPC APIs, or serverless architectures
• Familiarity with MLOps/LLMOps practices, including model versioning, experiment tracking, monitoring, and performance evaluation
• Understanding of system design, distributed computing, performance optimization, security, privacy, and responsible AI principles
• Experience working with large‑scale datasets, data pipelines, or big data/streaming technologies such as Spark, Databricks, or Kafka
• Familiarity with databases (SQL/NoSQL), version control, testing, CI/CD, and cloud‑native development practices