ML Engineer II
UST (company site, opens in a new tab)6+ yrsTrivandrumHybridDirect career pageEst. 20–35 LPA
Posted on 7 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, deploy, and optimize AI/ML and Generative AI solutions including LLMs, NLP, deep learning, RAG architectures, and vector databases in production environments.
- Establish and manage MLOps practices for model deployment, monitoring, lifecycle management, and ensure solutions meet security, compliance, scalability, and performance standards.
- Collaborate with cross-functional teams and mentor junior AI engineers while driving AI best practices and innovation initiatives.
Minimum Requirements
- 6-10+ years of relevant experience in AI/ML engineering or related roles.
- Strong proficiency in Python programming and hands-on experience with Generative AI, Large Language Models (LLMs), TensorFlow, PyTorch, Scikit-learn, LangChain, or LlamaIndex.
- Experience with MLOps, CI/CD pipelines, model deployment, and monitoring of production-scale AI systems.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
Ideal Candidate Profile
- Experienced in implementing Retrival-Augmented Generation (RAG) architectures and working with vector databases such as Pinecone, FAISS, Chroma, or Azure AI Search.
- Hands-on experience with cloud AI platforms like Azure AI, AWS AI/ML, or Google Cloud AI and familiarity with REST APIs, microservices, Docker, and Kubernetes.
- Background or preference for healthcare domain and knowledge of AI governance, responsible AI practices and enterprise AI solution delivery.