Key Responsibilities

• Develop and train machine learning models using Python, scikit-learn, and other ML frameworks.
• Conduct data wrangling, analysis, and feature engineering using Pandas, SQL, and IPython/Jupyter Notebooks.
• Architect and implement reproducible data pipelines with Kedro.
• Build and integrate intelligent applications leveraging Langchain and LLM ecosystems.
• Develop and deploy scalable ML services on AWS, utilizing services like S3, EC2, Lambda, SageMaker, etc.
• Containerize applications using Docker and orchestrate deployments with Kubernetes.
• Manage infrastructure as code using Terraform for provisioning cloud resources.
• Set up CI/CD pipelines for ML workflows using Jenkins or similar tools.
• Collaborate using Git for version control and code reviews.
• Monitor and optimize ML models in production for performance, drift, and reliability.
• Document technical designs, processes, and results to ensure reproducibility and knowledge sharing.
• Required Skills and Experience
• 10+ years of experience in Machine Learning Engineering, Data Engineering, or a similar technical role.
• Proficiency in Python and solid experience with libraries like scikit-learn, Pandas, and IPython/Jupyter Notebook for data analysis and modelling.
• Experience developing and orchestrating ML pipelines using Kedro or similar tools.
• Hands-on expertise deploying applications and ML services on AWS.
• Strong understanding of Docker and container-based deployments.
• Practical experience with Kubernetes for orchestration and scaling of ML workloads.
• Knowledge of Terraform for managing infrastructure as code.
• Experience setting up and managing CI/CD pipelines using Jenkins or equivalent tools.
• Solid SQL skills for data extraction, transformation, and analysis.
• Experience working with Langchain and LLM-based solutions is a significant plus.
• Familiarity with Git-based development workflows.
• Strong problem-solving skills and the ability to work independently and collaboratively.
• Nice to Have
• Experience with MLOps best practices (e.g., model versioning, monitoring, feature stores).
• Exposure to additional cloud platforms (e.g., GCP, Azure).
• Familiarity with security and compliance considerations in ML deployments.
• Experience working in Agile teams and collaborating closely with cross-functional stakeholders.
• At DXC Technology, we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We re committed to fostering an inclusive environment where everyone can thrive.