Machine Learning Engineer

As a Machine Learning Engineer at InMobi, you will be instrumental in designing, developing, deploying, and maintaining cutting-edge ML systems that power our product experiences. We're looking for someone with a passion for elegant problem-solving, rapid experimentation, and owning end-to-end machine learning workflows. You will work closely with data scientists and cross-functional teams to build robust data pipelines, scalable platforms, and production-ready ML services.
The core responsibilities for the job include the following:
Model Development and Deployment:
Design, train, and deploy machine learning models with a strong focus on fast, reliable experimentation.
Build APIs and microservices to serve ML models at scale.
Feature development and engineering:
Design and deliver tasks on backend features and efficiently deliver features.
Work closely with the analytics, architects, and PM to deliver feature requests as per ETA.
Identify solutions that can help us improve scalability, minimize bugs and reduce cost
Understanding of when to escalate questions/issues that arise during development
Able to efficiently diagnose bugs and issues
Familiar with various design and architectural patterns
Pipeline and Infrastructure:
Develop and maintain data ingestion, preprocessing, and model training pipelines.
Deploy scalable data and model solutions that improve efficiency across ML workflows.
Collaboration and Integration:
Work closely with Define and execute end-to-end ML solutions from ideation to production.
Monitoring and Research:
Monitor model performance in production, using statistical methods to ensure robustness.
Ownership and Delivery
Lead or contribute to POCs and full-scale ML feature rollouts.
Manage project deadlines and deliverables in an agile environment.
Requirements:
Bachelor's degree with 4+ years OR master's degree with 3+ years in computer science, machine learning, data science, or a related field.
Proven experience in developing and deploying ML models in production.
Strong proficiency in Python and ML libraries like TensorFlow, PyTorch, or scikit-learn, Spark, Java & distributed systems.
Understanding of statistical methods and hypothesis testing.
Comfortable working with structured and unstructured data.
Experience working in collaborative, cross-functional teams.
Loves to code and learn new concepts, technologies, and frameworks.
Preferred Qualifications:
Demonstrated ability to rapidly validate hypotheses through experimentation.
Experience in building recommendation systems or similar ML applications.
Exposure to advertising, ranking, or personalization systems is a significant plus.
Familiarity with SQL, data warehousing, and distributed data systems.
Prior research experience or involvement in a data science-focused role.
Strong mathematical foundation, particularly in statistics and linear algebra.
Excellent verbal and written communication skills.
Hands-on experience in Databricks is a plus.
Experience in the ad tech industry is a plus.
Experience: 2-5 years of development experience.
Education: B. E. /B. Tech in Computer Science or equivalent.
Strong development and coding experience in one or more programming languages like OO Programming (Java), Scala, Spark, and Python.
Expertise in Data Structures, Algorithms, and Concurrency.
Experience working on Big Data technologies and applications.
Experience in Micro-services Architecture, multi-threading, performance-oriented programming, and designing skills.
Good organization, communication, and interpersonal skills.
Must be a proven performer and team player that enjoys challenging assignments in a high-energy, fast-growing startup workplace.
Must be a self-starter who can work well with minimal guidance and in a fluid environment.
Provide good attention to details.
Must be excited by challenges surrounding the development of a highly scalable & distributed system for building audience targeting capabilities.
Agility and ability to adapt quickly to changing requirements and scope and priorities.
Nice to have skills:
Experience in the online advertising domain.
Experience of working on massively large-scale data systems in production environments.
Experience in leveraging user data for behavioral targeting and ad relevance.
Experience in the Big Data analytics domain.
Experience of building products that are powered by data and insights.
Experience in hosting and deploying applications on public clouds like Microsoft Azure, GCP, and AWS.

Experience
7-11 yrs