Senior AI Engineer.
This mandate is run by SYMPHONI HR, a Mumbai-based executive search firm, est. 2003. Applications are email-verified and reach the search team running the role — confidentially, always.
About the organisation.
Our client is a leading global computer software organization known for driving digital transformation through advanced technology solutions. They foster a culture of innovation and technical excellence, supporting large-scale enterprise projects across diverse industries. The company is committed to leveraging cutting-edge research to solve complex business challenges on a global scale.
About the role.
We are seeking a seasoned AI Engineer to lead the design and deployment of enterprise-grade artificial intelligence and generative AI solutions. This role involves architecting scalable systems, implementing agentic AI frameworks, and managing the full lifecycle of machine learning models. You will serve as a technical leader, bridging the gap between complex business requirements and high-impact AI implementations.
What you will do.
- Design and develop enterprise-grade Generative AI and Agentic AI applications.
- Build and deploy intelligent assistants, copilots, and automated reasoning frameworks.
- Implement Retrieval Augmented Generation (RAG) architectures for large-scale knowledge management.
- Optimize LLM performance, latency, and cost for production-ready environments.
- Design scalable data pipelines and AI-ready data platforms.
- Establish MLOps and CI/CD frameworks to ensure model reliability and governance.
- Lead technical architecture discussions and mentor junior data scientists and engineers.
- Collaborate with stakeholders to identify and deliver high-value AI use cases.
What you bring.
- 12-15 years of overall IT experience with at least 5 years in AI/ML development.
- Minimum 3 years of hands-on experience with Generative AI and Large Language Models.
- Expert-level proficiency in Python and strong experience with Java or Scala.
- Deep understanding of NLP, Deep Learning, and neural network architectures.
- Proven experience with cloud platforms, specifically Google Cloud Platform, AWS, or Azure.
- Strong background in Data Engineering, including Spark, Databricks, and vector databases.
- Experience implementing MLOps practices using tools like MLflow, Kubeflow, or Docker.
- Bachelor's or Master's degree in Computer Science, AI, or a related technical field.
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