About this role
Area(s) of responsibility
- Design and develop robust AI/ML solutions tailored to industry verticals such as Banking, Financial Services, Insurance (BFSI), Energy & Utilities (EnU), Life Sciences, and Manufacturing.
- Collaborate with cross-functional teams to gather business requirements and translate them into scalable machine learning architectures.
- Lead the end-to-end lifecycle of ML projects—from data exploration and model development to deployment and monitoring—ensuring alignment with organizational standards and best practices.
- Evaluate and select appropriate ML algorithms, frameworks, and tools to optimize model performance, scalability, and interpretability.
- Conduct research on emerging ML techniques and technologies, providing insights for continuous innovation and improvement.
- Define and implement data governance, model versioning, and deployment strategies to ensure compliance, reproducibility, and ethical use of AI.
- Mentor junior developers and data scientists, fostering a collaborative and innovative team culture.
- Communicate technical solutions and project progress to stakeholders and leadership, translating complex ML concepts into business-friendly language.
Must-Have Skills
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- 4+ years of experience in AI/ML development, with a strong focus on solution design, model development, and deployment.
- Proven experience building ML solutions across multiple verticals, especially BFSI, EnU, Life Sciences, and Manufacturing.
- Strong proficiency in programming languages such as Python or R, and hands-on experience with ML libraries like Scikit-learn, XGBoost, TensorFlow, or PyTorch.
- Solid understanding of supervised, unsupervised, and reinforcement learning algorithms.
- Experience with cloud platforms (AWS, Azure, GCP) and their ML services (e.g., SageMaker, Azure ML, Vertex AI).
- Strong analytical and problem-solving skills, with the ability to work in fast-paced environments.
- Excellent communication skills to convey technical concepts to non-technical audiences.
Good-to-Have Skills
- Experience with big data technologies (e.g., Spark, Hadoop) and data engineering workflows.
- Familiarity with MLOps practices and tools for CI/CD in ML pipelines (e.g., MLflow, Kubeflow, Airflow).
- Exposure to AI governance frameworks and responsible AI practices.
- Prior experience in consulting or client-facing roles delivering ML solutions.
- Advanced certifications in AI/ML or cloud technologies (e.g., AWS Certified ML Specialist, Azure AI Engineer Associate).