AI/ML Software Engineer

AI/ML Software Engineer Role Overview 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.