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Bangalore, Karnataka, India
Actively hiring, first seen by us todayBuild and deploy production LLM agents for issue detection, triage, and remediation
Design and tune RAG pipelines over runbooks, incident history, logs, and code
Implement agent orchestration, tool calling, memory, and multi-agent handoffs
Build evaluation pipelines and quality metrics for agent and RAG behavior
Ship agent capabilities into EVA for developers to use in their workflow
Build and deploy production LLM agents that automate detection, triage, and remediation of platform and application issues
Design and tune RAG pipelines over runbooks, incident history, logs, code, and internal documentation
Implement agent orchestration: tool/function calling, multi-step planning, multi-agent handoffs, memory, and state management
Build evaluation pipelines and define quality metrics for agent and RAG behavior
Ship agent capabilities into EVA so developers consume them inside their existing workflow
Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent experience
4+ years of software or machine learning engineering experience
2+ years hands-on building with LLMs, including prompt engineering, tool/function calling, and agent or multi-step orchestration
Demonstrated experience designing and shipping RAG systems in production, including embeddings, vector databases (for example Azure AI Search, Pinecone, Milvus, pgvector, or FAISS), and retrieval evaluation
Experience integrating third-party APIs and building backend services that run in production
Experience with cloud platforms (Azure, AWS, or GCP), containers, and CI/CD pipelines
Required Qualifications: - Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent experience - 4+ years of software or machine learning engineering experience
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 0
Early. Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
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Open jobs by the week we first saw them, last 12 weeks.
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- Experience building LLM evaluation and testing workflows rather than relying on manual spot checks - Proficiency in Python, with working knowledge of agent frameworks (for example LangGraph, LangChain, LlamaIndex, Semantic Kernel, or CrewAI) - Proven ability to work across teams and translate operational pain points into technical requirements
- Experience integrating third-party APIs and building backend services that run in production - Experience with cloud platforms (Azure, AWS, or GCP), containers, and CI/CD pipelines - Experience building LLM evaluation and testing workflows rather than relying on manual spot checks
- 2+ years hands-on building with LLMs, including prompt engineering, tool/function calling, and agent or multi-step orchestration - Demonstrated experience designing and shipping RAG systems in production, including embeddings, vector databases (for example Azure AI Search, Pinecone, Milvus, pgvector, or FAISS), and retrieval evaluation - Experience integrating third-party APIs and building backend services that run in production
Complete your application on careers.unitedhealthgroup.com. The employer’s form will show what is required.
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Yes. We’ve followed 21 other Senior AI/ML Engineer jobs there since August 2026. 10 came down, usually after about 23 days. 11 are still up.
If this one keeps the same pace, it may come down around November 27.
Large employers often post one title for several teams, so these can be separate openings.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.
Primary Responsibilities:
Contribute reusable patterns, components, and documentation for agent development across the Optum AI DLC
Required Qualifications:
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
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Bangalore, Karnataka, India
- Build and deploy production LLM agents that automate detection, triage, and remediation of platform and application issues - Design and tune RAG pipelines over runbooks, incident history, logs, code, and internal documentation, including chunking strategy, embedding selection, vector store design, hybrid and semantic search, and reranking - Implement agent orchestration: tool/function calling, multi-step planning, multi-agent handoffs, memory, and state management
No clear work-authorization passage found. Eligibility is unconfirmed.