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
Tags & focus areas
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