Machine Learning Engineer

Job Brief

In this role, we are looking for candidates who have relevant years of experience in designing and developing machine learning and deep learning system. Who have professional software development experience. Hands on running machine learning tests and experiments. Implementing appropriate ML algorithms engineers.

Employment Type: W2 Contract 

Key Responsibilities

· Design, build, and deploy scalable machine learning systems in production

· Lead the development and optimization of models for NLP, computer vision, recommendation, forecasting, or generative AI use cases

· Collaborate with product, data science, and engineering teams to translate business requirements into ML solutions

· Mentor junior engineers and contribute to technical strategy and roadmap planning

· Evaluate and implement state-of-the-art ML and deep learning frameworks and libraries

· Own the entire ML lifecycle: data collection, preprocessing, training, evaluation, deployment, monitoring, and retraining

· Drive innovation by staying up to date with the latest ML research and applying it to real-world problems

Required Qualifications

· 3-5 years of experience in software engineering and machine learning

· Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)

· Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (e.g., MLflow, Kubeflow, SageMaker)

· Solid understanding of statistical modeling, supervised and unsupervised learning, and deep learning architectures (CNNs, RNNs, Transformers)

· Demonstrated ability to deploy ML models in production environments at scale

· Experience working with large datasets, data pipelines, and distributed computing (Spark, Ray, etc.)

· Strong problem-solving skills and ability to communicate technical concepts clearly

Preferred Qualifications

• Mandatory certifications: AWS Certified Machine Learning, Microsoft Azure AI Engineer Associate, Google Cloud – Professional Machine Learning Engineer
• Master’s or PhD in Computer Science, Data Science, Applied Math, or related field
• Experience with LLMs, generative models, or foundation models (e.g., GPT, BERT, Stable Diffusion)
• Familiarity with containerization and orchestration (Docker, Kubernetes)
• Contributions to open-source ML projects or research publications