Senior Software Engineer – AI/ML

Job Description

Senior Software Engineer – AI/ML | InnovateTech Solutions

The Tone:
This is a full-time role at InnovateTech Solutions. InnovateTech Solutions is a technology company focused on Artificial Intelligence and Machine Learning. They develop solutions that aim to transform industries and improve lives globally. This role is crucial for designing, developing, and deploying advanced AI/ML models and systems that power next-generation products, directly contributing to core technological advancements and driving measurable impact.

The TL;DR
• Role: Full Time
• Type: Full-time
• Team: Innovative R&D team
• Mission: Design, develop, and deploy advanced AI/ML models and systems that power next-generation products.
• Tech Stack: Python, TensorFlow, PyTorch, scikit-learn, AWS, Azure, GCP, Docker, Kubernetes, Pandas, NumPy

What You’ll Actually Do
• Lead the design, development, and implementation of robust, scalable, and high-performance AI/ML models and algorithms.
• Architect and build end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment.
• Collaborate closely with product managers, data scientists, and other engineering teams to define requirements, design solutions, and deliver impactful features.
• Conduct thorough research and experimentation with new AI/ML techniques, frameworks, and tools to continuously improve capabilities.
• Optimize existing models and systems for performance, efficiency, and resource utilization.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field, with core domain knowledge in AI/ML engineering.
• Experience: 5+ years of professional experience in software development, with at least 3 years specifically focused on AI/ML engineering, including deploying ML models in a production environment.
• Skills: Strong proficiency in Python and AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn; solid understanding of machine learning principles, algorithms, and statistical modeling; experience with cloud platforms (AWS, Azure, GCP); familiarity with containerization technologies (Docker, Kubernetes); proficiency with data manipulation and analysis libraries (e.g., Pandas, NumPy).
• Bonus: Experience with MLOps practices and tools (e.g., MLflow, Kubeflow); familiarity with big data technologies (e.g., Spark, Hadoop); experience with real-time ML systems; publications in top-tier AI/ML conferences or journals; contribution to open-source projects; domain expertise in a specific industry.