Description
This is a highly skilled machine learning engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. This role sits at the intersection of data science, software engineering, and MLOps, and requires strong hands-on experience turning models into production-ready solutions, programming experience in Python or R.
Key responsibilities:
• Design, develop, train, and optimize machine learning models for real applications or use cases.
• Translate business and product requirements into scalable ML/AI solutions.
• Implement feature engineering, model selection, tuning, and evaluation techniques.
• Develop and deploy ML models into production environments with high availability and performance.
• Build and maintain ML pipelines (training, validation, deployment, monitoring).
• Monitor model performance, data drift, and model decay; retrain models as needed.
• Ensure models meet reliability, scalability, and security standards.
• Work closely with data scientists, product managers, and software engineers.
• Collaborate with data engineering teams to ensure high-quality, reliable data pipelines.
• Participate in design and code reviews, ensuring engineering best practices.
• Optimize models for latency, throughput, and cost.
• Implement experimentation frameworks (A/B testing, offline evaluation).
• Apply responsible AI principles, including fairness, explainability, and governance where required.
Requirements
• 3–7+ years of hands-on experience in machine learning or applied AI roles.
• Strong programming skills in Python (and/or Java, Scala).
• Solid understanding of ML algorithms (supervised, unsupervised, deep learning).
• Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Experience deploying models using Docker, Kubernetes, or cloud ML services.
• Strong knowledge of data structures, algorithms, and software engineering principles.
• Experience working in agile, cross-functional teams.
• Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
• Experience with big data technologies (Spark, Kafka, Databricks).
• Background in NLP, computer vision, or generative AI.
• Strong problem-solving and analytical thinking.
• Production-first mindset.
• Data-driven decision making.
• High collaboration and communication skills.
