Senior Software Engineer (Machine Learning)

Summary of the position
We are seeking a passionate and technically strong Senior Software Engineer (Machine Learning) to design, build, and deploy scalable ML based systems and services. The role involves working across the entire ML lifecycle from data ingestion and feature engineering to model development, optimization, and deployment with a focus on delivering high-quality, production-grade solutions.

The ideal candidate is an experienced engineer who combines machine learning expertise with solid software engineering skills. You will work closely with data scientists, and engineering teams to translate business requirements into data-driven solutions while ensuring scalability, performance, and maintainability.
Key accountabilities

• Machine Learning Development:
  • Design and implement machine learning mdels and algorithms for predictive analytics, classification, clustering, or recommendation systems.
  • Optimize mdels for accuracy, performance, and scalability in production environments.
  • Develp automated pipelines for model training, validation, and deployment (ML pipelines).
  • Cnduct feature selection, model tuning, and hyperparameter optimization.
  • Cllaborate with data scientists to transition research models into production-ready services.
• Engineering & Deployment:
  • Build and maintain APIs r microservices for model inference and integration with other systems.
  • Wrk closely with DevOps to deploy, scale, and monitor ML workloads on cloud platforms (AWS/Azure).
  • Ensure prper data handling, storage, and lineage across training and inference pipelines.
• Data Engineering & Collaboration:
  • Wrk with data engineers to build and optimize ETL/ELT pipelines for ML-ready data.
  • Ensure data quality, cnsistency, and governance throughout the ML lifecycle.
  • Cllaborate with cross-functional teams to define problem statements, metrics, and success criteria.
• Innovation & Continuous Improvement:
  • Explre and evaluate new ML techniques, frameworks, and architectures.
  • Drive autmation and standardization in ML workflows to improve efficiency.
  • Contribute to the organization’s engineering best practices and knowledge sharing.
  
  

Skills and Experience | Essential

• Core Technical Skills:
  • 4 t 6 years of experience in software engineering, with at least 2 years in machine learning development.
  • Prficiency in Pythn and key ML libraries like Scikit-learn, TensrFlow or PyTorch.
  • Strng understanding of data preprocessing, feature engineering, and model lifecycle management.
  • Prficiency in SQL and expsure to NSQL systems.
  • Experience building APIs r backend services using frameworks like FastAPI, Flask, r Djang.
• Cloud Exposure:
  • Wrking knowledge of AWS Sagemaker/Azure ML.
  • Experience in cntainerization (Docker, Kubernetes) and integrating ML mdels into production systems.
  • Familiarity with CI/CD tols and version control (Git).
• LLM & AI Awareness (Preferred):
  • Awareness f Large Language Mdels (LLMs) such as OpenAI, LLaMA, and Bedrck.
  • Ability t evaluate and recommend the appropriate model for a given use case.
• Visualization & Communication:

Experience using Power BI, Tableau, or Python visualization