Software Engineer I – (Python, LLMs & Statistical Modelling)

Overview:

In this role. You will be responsible for designing, building, and deploying applications in our next-gen Data Integrity Suite platform based on k8's, and Machine learning. You will have the opportunity to apply your analytical, statistical, and programming skills to analyze large datasets and contribute to developing innovative, data-centric solutions for real-world business challenges. We are looking for a candidate with a robust logical aptitude, a foundational understanding of machine learning concepts, and an eagerness to learn and grow. You will work closely with Software engineers, data scientists, and product managers to develop and deploy data-driven solutions that deliver business value.

What you will do:

• Collaborate closely with Technical Architects and Principal Engineers to design, build, and deploy AI applications.
• Contribute to the development of our next-generation Data Integrity Suite platform.
• Apply analytical, statistical, and programming skills to solve complex, real-world business challenges.
• Focus on the Location Intelligence domain to deliver innovative, data-centric solutions.
• Participate in end-to-end solution development—from concept to deployment—ensuring scalability and performance.
• Work in a cross-functional team environment to align technical solutions with business goals.

What we are looking for:

• 1-3 Year of experience in Software development life cycle.
• Bachelor's degree in Statistics, Mathematics, Computer Science, or another quantitative field from a Tier-1 college
• Strong fundamentals in data structures, algorithms, and complexity analysis
• Strong understanding of Programing languages – Java, Django
• Experience building production quality RESTful APIs.
• statistical computer language (Python, SQL, etc.) to manipulate data and draw insights.
• Build process understanding – Jenkins, maven
• Understanding of Cloud technology – AWS
• Familiarity with fundamental machine learning techniques (e.g., clustering, decision tree learning, artificial neural networks) and an understanding of their practical applications.
• Good grasp of core statistical concepts (e.g., regression, distributions, statistical tests).
• Preferred Requirements:
  • Understanding of R
  • Exposure to data visualization tools such as Qlik, Streamlit, or GGPlot.
  • Familiarity with deep learning frameworks like
  • Keras, PyTorch, or TensorFlow, and libraries such as OpenCV.
  • Prior experience or interest in the geo-spatial domain (e.g., geocoding, location intelligence) is a significant plus.
  • Familiarity with LLM-based applications.
  • Experience in Microservices, Caching technologies.
  • Experience with Docker, Kubernetes, CI/CD.
  • Experience with helm as package manager.
  • Tools: Maven, Gradle, Jenkins, GIT.
  • Experience working in an agile development environment.

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