Senior Software Engineer – AI/ML Platform

Job Description

Senior Software Engineer – AI/ML Platform | Not Specified

The Tone:
This is a full-time role at Not Specified, with remote-friendly policies. The company develops cutting-edge AI solutions that transform industries. This role is crucial for designing, developing, and maintaining scalable and robust AI/ML infrastructure and services that support the company’s ambitious goals.

The TL;DR
• Role: Full Time
• Type: Flexible
• Location: Remote-friendly

• Team: Dynamic cross-functional team
• Mission: Owns the design, development, and maintenance of scalable and robust AI/ML infrastructure and services.
• Tech Stack: Python, Java, Go, C++, AWS, GCP, Azure, Docker, Kubernetes, TensorFlow, PyTorch, scikit-learn, Kafka, Spark Streaming, PostgreSQL, MongoDB, Cassandra

What You’ll Actually Do
• Platform Development: Lead the design and implementation of core components for the AI/ML platform, ensuring performance, scalability, security, and reliability.
• Service & Pipeline Development: Develop and maintain API services and data pipelines for machine learning model training, deployment, monitoring, and inference.
• Model Productionization: Collaborate with Data Scientists and ML Engineers to translate research prototypes and models into production-ready systems.
• Architectural Strategy: Contribute significantly to the architecture, technical roadmap, and strategic planning of AI/ML infrastructure.
• Technical Leadership: Mentor junior engineers, foster knowledge sharing, and perform rigorous code reviews to ensure code quality and adherence to best practices.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field, with a significant focus on backend systems and distributed architectures.
• Experience: 5+ years of professional software development experience, with a proven track record of building, deploying, and maintaining scalable, high-performance services and APIs.
• Skills: Strong proficiency in Python and experience with at least one other language (e.g., Java, Go, C++), extensive experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), infrastructure as code, familiarity with machine learning frameworks (TensorFlow, PyTorch, scikit-learn), MLOps principles, data structures, algorithms, and object-oriented design.
• Bonus: Experience with distributed stream processing (Kafka, Spark Streaming), big data systems, CI/CD pipelines, automated testing frameworks, observability tools, contributions to open-source projects, database technologies (PostgreSQL, MongoDB, Cassandra), or prior leadership roles.