AI Engineer / ML Engineer

Full 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