Key Responsibilities:
• Design, develop, and implement AI and Generative AI solutions using LLMs, Agentic AI frameworks, RAG architectures, and cloud AI platforms.
• Develop and maintain scalable AI/ML pipelines, data preparation workflows, and cloud-native integrations using platforms such as Azure, Databricks, and AWS.
• Assist in integrating AI capabilities with APIs, databases, cloud services, and internal applications.
• Support deployment, testing, monitoring, and operational maintenance of AI/ML solutions following MLOps and LLMOps best practices.
• Implement Responsible AI and AI governance practices including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards to support transparency and fairness of deployed AI solutions.
• Stay updated on emerging trends and technologies related to Generative AI, LLMs, Agentic AI and cloud platforms
Preferred Qualifications:
• Bachelor’s degree in Computer Science, Data Science, AI/ML, or a related technical field.
• Hands-on experience in developing and successfully deploying production-level AI applications
• Experience with Python, Spark or SQL.
• Basic understanding of LLMs, Generative AI concepts, prompt engineering, and AI/ML workflows.
• Exposure to at least one cloud platform or AI ecosystem such as Azure, AWS, Databricks, Microsoft Fabric, or Microsoft Copilot technologies, along with familiarity with containerization technologies such as Docker or Kubernetes.
• Familiarity with APIs, data pipelines, ETL processes, or cloud-based integrations.
• Exposure to AI/ML frameworks, vector databases, or orchestration frameworks such as LangChain or similar technologies is a plus.
• Understanding of software development lifecycle, deployment processes, and version control tools such as Git.
• Strong analytical, problem-solving, and communication skills.
• Eagerness to learn emerging AI technologies and work in a fast-evolving AI engineering environment.