Senior AI Engineer
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- DockerPythonJavaKubernetesMLOpsLangChain
Requirements
- 10+ years of experience building large-scale cloud-based distributed systems.
- Strong expertise in Python and Java for backend and AI system development.
- Hands-on experience with LLMs, RAG architectures, LangChain, and LlamaIndex.
- Proven experience designing and building multi-tenant SaaS platforms.
- Strong understanding of microservices architecture and cloud-native design patterns.
- Experience with NLP tools and frameworks such as Hugging Face, spaCy, or NLTK.
- Solid experience with Kubernetes, Docker, and CI/CD pipelines in production environments.
- Strong knowledge of data pipelines, ETL workflows, and data storage systems (SQL/NoSQL).
- Familiarity with model deployment frameworks and MLOps practices (MLflow, TensorFlow Serving, etc.).
- Strong analytical thinking, debugging skills, and ability to solve complex distributed system challenges.
- Excellent communication skills and ability to work effectively in distributed, remote-first teams.
- Strong mentoring mindset and ability to contribute to team growth and technical excellence.
Responsibilities
- Design, build, and maintain scalable AI-powered backend systems and services within a multi-tenant SaaS architecture.
- Develop and deploy AI solutions leveraging LLMs, RAG pipelines, and frameworks such as LangChain and LlamaIndex.
- Architect and implement robust, production-ready distributed systems ensuring high availability, scalability, and performance.
- Collaborate with product, engineering, and operations teams to define, design, and deliver AI-driven features.
- Build and optimize data pipelines, ETL processes, and integrations supporting AI model training and inference.
- Deploy and manage containerized applications using Docker and Kubernetes in cloud environments (AWS/Azure).
- Work with technologies such as Python, Java, NLP libraries, and modern ML frameworks for AI system development.
- Ensure system reliability, observability, and security across AI services operating at enterprise scale.
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