ML Engineer
Role Overview
We are seeking a talented Data Scientist / Machine Learning Engineer with 3–6 years of hands-on experience to join our growing Data & AI Engineering team. The right candidate is someone who has strong expertise in building scalable machine learning models and data pipelines, and is passionate about leveraging data to solve complex business problems.
This role is suited for someone who enjoys working with large datasets, modern ML frameworks, and cloud-based data platforms to build intelligent solutions that drive real business impact.
Roles & Responsibilities
Design, develop, and deploy machine learning models for tasks such as classification, prediction, scoring, and recommendation systems.
Build and optimize scalable data pipelines and data processing workflows to support machine learning and analytics initiatives.
Work with large-scale datasets using frameworks such as Spark, Hadoop, Kafka, or Kinesis to process structured and unstructured data efficiently.
Develop and implement deep learning models, including CNNs and NLP-based architectures, for advanced analytics and AI-driven applications.
Leverage AWS services such as EC2, EMR, Redshift, RDS, and AWS AI/ML services to build and deploy scalable machine learning solutions.
Develop efficient data models and optimize query performance across SQL and NoSQL databases.
Work closely with cross-functional teams and external stakeholders to understand requirements, translate business problems into data-driven solutions, and deliver impactful outcomes.
Continuously evaluate model performance and improve solutions through experimentation, feature engineering, and model tuning.
Requirements
Qualifications & Skills
Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Informatics, Information Systems, or a related quantitative field.
3–6 years of experience in a Data Scientist, Machine Learning Engineer, or similar role.
Strong programming skills in Python, with familiarity in Java or Scala being a plus.
Advanced knowledge of SQL and experience working with relational databases such as MySQL or PostgreSQL.
Experience with NoSQL databases such as DynamoDB or Cassandra.
Hands-on experience with big data technologies such as Spark, Hadoop, Kafka, Kinesis, or Elasticsearch.
Experience building and optimizing data pipelines, architectures, and large-scale datasets.
Strong understanding of machine learning algorithms and model development workflows.
Experience with deep learning frameworks and neural networks including CNNs and NLP models.
Experience leveraging AWS AI/ML and data services to develop and deploy machine learning solutions.
Familiarity with message queues, stream processing, and distributed data systems.
Strong analytical, problem-solving, and communication skills.
Ability to work effectively in fast-paced, collaborative environments.
Preferred Certifications
AWS Certified Machine Learning – Specialty (Strongly Preferred)
AWS Certified Solutions Architect – Associate (Strongly Preferred)
Signs You May Be a Great Fit
Impact: Play a key role in building AI-driven solutions that power data-driven decision-making and intelligent automation.
Culture: Work in a collaborative, innovative environment that encourages experimentation and continuous learning.
Growth: Gain exposure to cutting-edge ML technologies, large-scale data platforms, and real-world AI use cases.
Benefits: Competitive compensation, professional development opportunities, and a flexible work environment designed to support your growth.