AI Engineer
At a glance
Mid-level AI Engineering role at Ecolab. Bangalore · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build LLM-powered applications for summarization, question answering and decision support
- Create multi-step automated workflows using agent orchestration
- Connect embedding pipelines to vector stores for retrieval-augmented generation and search
- Package AI components for cloud deployment and use container-based CI/CD practices
- Track production model behavior, then tune performance and cost efficiency
What you bring
- You have two to four years building AI or machine learning applications
- You can code in Python and connect LLM APIs through prompt workflows
- You have hands-on Databricks and Snowflake experience
- You understand agent orchestration, embeddings, vector databases and agentic tools
- You have delivered cloud-native systems with Azure, CI/CD and API integration patterns
Who this fits
This role suits a mid-level AI engineer who combines hands-on development with solution design. The position is based in Bangalore, India.
From the employer
AI Engineer
As an AI Engineer, you will design, implement, and deploy intelligent systems powered by LLMs, agent orchestration frameworks, and embedding-based retrieval solutions. This role blends hands-on engineering with solution thinking, focusing on scalable workflows and cloud-native delivery.
Core Responsibilities
• Build applications leveraging LLM APIs for summarization, Q&A, and decision support
• Implement agentic workflows for multi-step task automation
• Develop embedding pipelines integrated with vector stores for RAG and search optimization
• Deploy AI components into cloud platforms using containerized CI/CD practices
• Monitor model behaviors in production and optimize for performance and cost-efficiency
Required Skills
• 2–4 years of experience in AI/ML application development
• Strong programming skills in Python with LLM API integration and prompt workflows
• Hands-on experience with Databricks (Spark SQL, PySpark, Delta Lake) and Snowflake
• Familiarity with agent orchestration, embeddings, vector databases, and agentic tools
• Experience with cloud-native delivery (Azure), CI/CD, and API integration patterns