ML Engineer

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.

• 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.

• 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 Tensor Flow, 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, Sage Maker, 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.