About The Opportunity
A leading technology consulting firm driving AI-powered transformation across finance, healthcare, retail, and manufacturing sectors. We deliver scalable machine-learning solutions—ranging from predictive analytics to recommendation engines and computer vision applications—for global enterprise clients.
Our Machine Learning team is expanding to architect, build, and deploy end-to-end ML pipelines on-site in India. If you thrive on hands-on model development, scalable deployments, and continuous optimization, join us to accelerate data-driven strategies.
Role & Responsibilities
- Translate business requirements into scalable ML models and end-to-end pipelines.
- Develop, train, and validate supervised and unsupervised models using Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Design and implement data preprocessing and feature engineering workflows to ensure high data quality.
- Deploy models as RESTful services using Docker, Kubernetes, and cloud-native technologies (AWS/GCP/Azure).
- Monitor model performance in production, implement A/B testing, retraining pipelines, and performance tuning.
- Collaborate cross-functionally with Data Engineers, DevOps, and Product teams to integrate ML components into applications.
Skills & Qualifications
Must-Have
- Bachelor’s or Master’s in Computer Science, Engineering, or related field.
- Proficient in Python and core ML libraries: TensorFlow, PyTorch, scikit-learn.
- Hands-on experience designing, training, and evaluating ML models (classification, regression, clustering).
- Strong expertise in data preprocessing, feature engineering, and pipeline development.
- Experience deploying models using Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
- Solid understanding of software engineering best practices: Git, CI/CD, and unit testing.
Preferred
- Experience with MLOps frameworks: Kubeflow, MLflow, or SageMaker.
- Familiarity with NLP, computer vision, or time-series forecasting.
- Knowledge of big data technologies: Spark, Hadoop, or Airflow.
- Experience automating monitoring and retraining pipelines.
- Exposure to API design and microservices architecture.
- Certifications in cloud ML services (AWS Certified ML Specialty, Azure AI Engineer).
Benefits & Culture Highlights
- Competitive salary, comprehensive health insurance, and performance-based bonuses.
- Collaborative on-site environment with continuous learning, mentorship, and knowledge-sharing.
- Access to cutting-edge AI tools, industry conferences, and certification support.
Skills: python,tensorflow,pytorch,scikit-learn,machine learning,ml,data science
