Responsibilities
- Design and build modular, reusable AI components that can scale across business units.
- Lead development of scalable RAG-based solutions for document comparison and analysis.
- Work with structured and unstructured data to support AI-driven business solutions.
- Engineer multi-agent systems for intelligent task coordination and decision support.
- Develop transcription and NLP pipelines for customer interaction analysis.
- Build, fine-tune, and deploy models using tools and frameworks such as PyTorch, Transformers, and LangChain.
- Package models for deployment in GCP, Azure ML, and/or Databricks.
- Integrate with Databricks for data ingestion, feature engineering, experimentation, and model development.
- Work closely with MLOps, DevOps, and Data Engineering teams to align infrastructure and deployment patterns.
- Contribute to shared libraries, APIs, templates, and reusable frameworks that accelerate AI product delivery.
- Provide technical guidance to teams adopting reusable AI components.
- Ensure AI products meet enterprise-grade security, compliance, scalability, and maintainability standards.
- Implement monitoring for model performance, data drift, usage metrics, and production reliability.
Basic qualifications
- Experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or similar technical role.
- Strong experience building production-grade AI/ML solutions.
- Hands-on experience with cloud-based AI services, preferably GCP or Azure .
- Experience developing RAG-based applications using structured and unstructured data.
- Strong knowledge of machine learning, NLP, LLMs, and modern AI application patterns.
- Experience with frameworks and tools such as: PyTorch Transformers LangChain
- PyTorch
- Transformers
- LangChain
- Experience deploying models in cloud or enterprise environments.
- Strong programming and software engineering skills.
- Ability to work with APIs, reusable components, and scalable architectures.
- Experience collaborating with MLOps, DevOps, and data engineering teams.
- Strong analytical, problem-solving, and communication skills.
Preferred qualifications
- Experience with Azure ML, GCP Vertex AI, Databricks, or similar platforms.
- Experience designing multi-agent systems or AI orchestration workflows.
- Experience developing transcription, NLP, or customer interaction analytics pipelines.
- Experience with model monitoring, data drift detection, observability, and usage metrics.
- Experience building shared AI libraries, reusable templates, or internal AI platforms.
- Understanding of enterprise security, compliance, and governance requirements for AI products.
- Product mindset with the ability to design AI solutions that are reusable, scalable, and business-focused.
Tags & focus areas
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