Machine Learning Engineer

• Design and develop scalable machine learning models and deploy them in production 
environments.
• Build and implement agentic systems that can autonomously analyze tasks, process large 
volumes of unstructured data, and provide actionable insights.
• Collaborate with data scientists, software engineers, and domain experts to integrate AI 
capabilities into cutting-edge products and solutions.
• Develop deterministic and reliable AI systems to address real-world challenges.
• Create and optimize scalable, distributed ML pipelines.
• Perform data preprocessing, feature engineering, and model evaluation to ensure high 
performance and reliability.
• Stay abreast of advancements in AI technologies and incorporate them into business solutions.
• Participate in code reviews, contribute to system architecture discussions, and continuously 
enhance project workflows.
Required Skills and Qualifications
• Software Engineering Fundamentals: Strong foundation in algorithms, data structures, and 
scalable system design.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or related fields 
with a solid academic track record.
• Experience: 2-5 years of hands-on experience in building AI systems or machine learning 
applications.
• Agentic Systems: Proven experience in developing systems that utilize AI agents for automating 
complex workflows, analyzing unstructured data, and generating actionable outcomes.
• Programming: Proficiency in programming languages such as Python, Java, or Scala.
• AI Expertise: Experience with machine learning frameworks like TensorFlow, PyTorch, or 
Hugging Face libraries (e.g., for working with transformer-based models and LLMs).
• MLOps Knowledge (preferred): Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, 
or Kubernetes.
• Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud for deploying machine 
learning models.
• Big Data: Experience with data processing tools and platforms such as Apache Spark or Hadoop.
• Problem-Solving: Strong analytical and problem-solving skills, with the ability to create robust 
solutions for complex challenges.
• Collaboration and Communication: Excellent communication skills to articulate technical 
ideas effectively to both technical and non-technical stakeholders