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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. The AI/ML Engineer is a primary driver in the design and development of state-of-the-art Artificial Intelligence solutions for medical applications. The Principal Data Scientist ML/DL works closely with senior data scientists, machine learning engineers, software engineers and subject matter experts on current company technologies and forward-looking projects. They are the drivers of new research and solution implementation in the creation of novel artificial intelligence approaches. Primary responsibilities include the enhancement of existing company NLP technologies and extension of those systems in new cloud-based applications. Emphasis is on development of novel machine/deep learning techniques for information extraction and synthesis. They translate research code into clinical NLP solutions deployed at scale in production environments including statistical methods, deep learning, and large language model technologies. Work will involve all aspects of methods development from initial PoC implementation to performance characterization and production launch of new methods. The successful candidate will have a solid history of publication in Machine/Deep Learning with an emphasis on Natural Language Processing, Information Retrieval and/or Information Extraction. Exposure to recent research literature and the ability to effectively implement new technologies is key. The successful candidate will have proven success in taking machine/deep learning solutions to production environments. Solid technical skills are required. Primary Responsibilities: Develop end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystemsDemonstrated publication record in AI domain especially relating to text extraction and summarizationArchitect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual groundingDesign, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrievalDevelop semantic retrieval systems with advanced document segmentation and indexing strategiesBuild and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node trainingApply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goalsCollaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environmentsComply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do soRequired Qualifications: Bachelor's degree in computer science, Machine Learning, or related field5+ years of experience in applied AI/ML with statistics, with a solid track record of delivering production-grade modelsExperience with PyTorchExperience with Hybrid NLP solutions that combine symbolic and machine learning approachesDeep expertise in: NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture Azure ML and/or AWSSolid in Python coding, SQL and database queries, data preparation, and analysisExploratory Data Analysis (EDA)Embedding models (e.g., BGE, E5, SimCSE)Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)Model fusion and ensemble techniques (stacking, boosting, gating)Deep knowledge and extensive experience with Machine/Deep Learning frameworks including transformer architectures, state space models, large language models, and agentic approachesKnowledge of algorithms and techniques within a computational domain with emphasis on text processing Preferred Qualifications: Experience with healthcare data and medical coding systems (e.g., CPT, CM, PCS)Document segmentation and preprocessing (OCR, layout .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.