AIML Developer

Responsibilities

• 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

• Bachelors or Masters 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).