Senior AI Engineer
New
J
JobgetherEnterprise AI
Based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of software engineering experience
- Required Skills
- PythonCI/CDMicroservicesMLOps
Requirements
- Have 5+ years of software engineering experience.
- Have significant hands-on experience building AI, machine learning, or advanced analytics solutions.
- Demonstrate strong proficiency in Python and modern AI development frameworks, libraries, and tooling.
- Have experience developing, deploying, and supporting production-grade AI, machine learning, or data-driven solutions in cloud environments.
- Understand software engineering practices, architecture patterns, APIs, microservices, CI/CD, and cloud-native development.
- Understand modern AI methodologies, including LLMs, semantic models, knowledge and context graphs, RAG, and agentic AI architectures.
- Translate complex business needs into scalable technical solutions.
- Experience with AI solutions for regulated industries such as life sciences, diagnostics, healthcare, or advanced manufacturing is a plus.
- Experience developing AI capabilities for knowledge extraction, decision support, process optimization, or workflow automation across enterprise data ecosystems is desirable.
- Knowledge of Responsible AI and enterprise AI governance, including model evaluation, explainability, bias assessment, and compliance with emerging AI regulations and frameworks, is an advantage.
Responsibilities
- Design, develop, and deploy scalable AI solutions, applications, services, workflows, and reusable capabilities.
- Develop and optimize LLM applications using prompt engineering, RAG, fine-tuning, and agentic workflows.
- Build AI capabilities that can be reliably deployed and supported in production and enterprise environments.
- Implement and support MLOps, DataOps, and LLMOps practices for model lifecycle management, deployment, monitoring, governance, and continuous improvement.
- Contribute to scalable, reliable, maintainable, and secure AI architectures that meet enterprise standards.
- Collaborate with Product Owners, Architects, AI Scientists, Platform Engineering, and Data Engineering teams to scale AI solutions.
- Translate business opportunities into practical AI applications, agents, and reusable technical capabilities.
- Apply software engineering practices, architecture patterns, APIs, microservices, CI/CD, and cloud-native development principles to AI solutions.
- Improve AI engineering standards, processes, and reusable capabilities across the enterprise.
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