ML Engineer II - Data Engineer +AI
UST (company site, opens in a new tab)BengaluruOn-siteDirect career pageEst. 12–22 LPA
Posted on 10 Sept
Reach people at UST
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at UST you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, test, and deploy scalable AI/ML and generative AI solutions including LLM applications and RAG solutions to improve business outcomes.
- Build and optimize data pipelines supporting AI model training and inference; create scalable APIs and AI services for enterprise use.
- Implement MLOps best practices, validate agentic AI outputs, ensure solution compliance with security and responsible AI standards, and collaborate with cross-functional teams to translate requirements into AI solutions.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
- Strong programming skills in Python and SQL; experience with Large Language Models (OpenAI, Anthropic, Llama) and prompt engineering.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform; knowledge of REST APIs, Git, and software development best practices.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced in deploying and managing AI-powered applications with expertise in Large Language Models and prompt engineering.
- Skilled in building scalable data pipelines and APIs, with familiarity in MLOps, agentic AI testing, and AI governance standards.
- Operates effectively in cross-functional teams translating business needs into AI solutions, with knowledge of modern data platforms like Snowflake and Databricks, and AI orchestration frameworks (LangChain, LangGraph, Semantic Kernel).