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Remote Retrieval Augmented Generation Jobs in Chicago, IL

Morningstar

Senior Software Engineer

Chicago, IL · On-site +1

$153K/yr

Remote work requests will be considered consistent with company's remote work policy. Job ... Multiprovider integration (OpenAI, Anthropic, MistralAI, etc.), Retrieval augmented generation ...

Okta

Senior Software Engineer, AI

Chicago, IL · Remote

$126K - $166K/yr

Build and maintain retrieval-augmented generation (RAG) pipelines with vector search and embedding strategies (e.g., Pinecone, FAISS, Qdrant). * Collaborate with designers and product managers to ...

Performacentric

Full Stack Developer

Chicago, IL · On-site +1

Remote (Preferred: Philippines, Latin America, or North America) Employment Type: Full-Time / ... Build retrieval-augmented generation (RAG) solutions. * Support prompt engineering and AI workflow ...

Pryon

Solutions Architect

Chicago, IL · Remote

$160K - $180K/yr

Now we're building an industry-leading knowledge management and Retrieval-Augmented Generation (RAG ... Benefits for Full Time Employees: - Remote first organization - 100% Company paid Health/Dental ...

Pryon

Solutions Architect

Chicago, IL · Remote

$160K - $180K/yr

Now we're building an industry-leading knowledge management and Retrieval-Augmented Generation (RAG ... Remote first organization - 100% Company paid Health/Dental/Vision benefits for you and your ...

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Remote Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Remote Retrieval Augmented Generation Engineer, and why are they important?

To thrive as a Remote Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and experience with retrieval systems such as Elasticsearch or FAISS are typically required. Problem-solving, effective communication, and adaptability are important soft skills for collaborating remotely and iterating on rapidly evolving AI solutions. These skills ensure the engineer can design, deploy, and optimize robust RAG systems that effectively combine retrieval and generation for high-quality AI outputs.

What is the difference between Remote Retrieval Augmented Generation vs Remote Data Scientist?

AspectRemote Retrieval Augmented GenerationRemote Data Scientist
CredentialsAI/ML knowledge, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development, NLP projectsData analysis, model building
Industry UsageAI, NLP, machine learningTech, finance, healthcare
Search & ComparisonOften compared for AI roles involving language modelsCompared for data analysis roles

Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.

What are some common challenges faced by professionals working in Remote Retrieval Augmented Generation roles, and how can they be addressed?

Professionals in Remote Retrieval Augmented Generation (RAG) roles often encounter challenges related to integrating diverse data sources, ensuring low latency in information retrieval, and maintaining the quality and relevance of augmented outputs. Coordinating effectively with distributed teams and adapting to rapidly evolving AI technologies are also common hurdles. To address these, staying current with best practices in data engineering, leveraging robust APIs, and participating in regular team check-ins can help ensure smooth collaboration and system performance.

What is Remote Retrieval Augmented Generation?

Remote Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models with external information sources. In a remote RAG setup, the model retrieves relevant data from remote databases or APIs during the generation process, enhancing its responses with up-to-date or domain-specific knowledge. This approach is widely used in applications that require accurate, context-aware answers, such as chatbots, search engines, and virtual assistants. By leveraging remote retrieval, RAG systems can access a broader range of information without needing to store all data locally.
What are the most commonly searched types of Retrieval Augmented Generation jobs in Chicago, IL? The most popular types of Retrieval Augmented Generation jobs in Chicago, IL are:
What are popular job titles related to Remote Retrieval Augmented Generation jobs in Chicago, IL? For Remote Retrieval Augmented Generation jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Remote Retrieval Augmented Generation jobs in Chicago, IL look for? The top searched job categories for Remote Retrieval Augmented Generation jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Remote Retrieval Augmented Generation jobs? Cities near Chicago, IL with the most Remote Retrieval Augmented Generation job openings:
Remote Retrieval Augmented Generation jobs near you

Senior Software Engineer

Morningstar

Chicago, IL • On-site, Remote

$153K/yr

Full-time

Medical, Dental, Life, Retirement

Re-posted 6 days ago

MorningStar Senior Living rating

6.4

Company rating: 6.4 out of 10

Based on 29 frontline employees who took The Breakroom Quiz

85th of 240 rated social care providers

Job description

Job Duties:

Architecting, optimizing and developing Python-based applications and APIs (FastAPI, Flask, RESTful services), including asynchronous programming and event-based architectures using cloud-native services (20%). Architecting, optimizing relational and vector databases (PostgreSQL, SQLAlchemy, query optimization, indexes, replicas, migrations, Weaviate, Pinecone) and working with dataframes for data processing and analysis (SQL-based agents) (20%). Driving AI security, compliance, and governance strategies (hallucination mitigation, ethical AI practices, AI guardrails) (10%). Architecting, researching and reviewing AI-driven enterprise platforms (retrieval-augmented generation, LLM fine-tuning, AI governance, model optimization) (20%). Defining and reviewing technical documentation, setting architectural guidelines, enforcing best coding practices, conducting design reviews, and ensuring maintainability and scalability of codebases (10%). Collaborating with cross-functional teams to align AI strategies with business needs and technical requirements (20%). **Remote work requests will be considered consistent with company's remote work policy.

Job Requirements:

This position requires a bachelor's degree in computer science, or a related field, or foreign equivalent and 5 years of relevant experience as a Software Engineer, Application Development Associate, or in a related position. In alternative, we accept a Master's degree in Computer Science, or a related field, or foreign equivalent and 3 years of relevant experience as a Software Engineer, Application Development Associate, or in a related position.

This position also requires database engineering management through RDBMS (SQL Server, PostgreSQL) including design, normalization, optimization, sharding, ACID transactions, and migrations. Python Development: Production applications, APIs (calling and invoking, Rest API's) for data preprocessing. Object-oriented programming in Python/Java, including OOP design patterns and UML architecture. Data processing and visualization by using QlikView and Python (Pandas, Plotly, Matplotlib). Agile development practices with emphasis on customer-centric delivery. Cloud and infrastructure management by using various cloud services such as AWS S3, Aurora, RDS, API Gateway, and AWS Lambda. Machine learning and statistical methods, including natural language processing (NLP) and embeddings. Version control and CI/CD (Git, application deployment and monitoring tools). Authoring technical documentation for developers, technical, and non-technical users. Vector Databases & Retrieval: Weaviate, Pinecone, GraphQL-based querying, AI-powered retrieval. Scalability & Performance: Queuebased request handling (SQS, Celery), event-driven architectures, caching using in-memory data structures such as Redis. AI Adoption: Driving AI tool adoption within enterprises. Multiprovider integration (OpenAI, Anthropic, MistralAI, etc.), Retrieval augmented generation, function calling, structured outputs, conversational memory. Prompt Engineering: Chain-ofthought prompting, prompt caching, zero-shot prompting. Agentic Frameworks: LangGraph or AutoGen for building agentic orchestrations. Feature Flagging tools such as Split or CloudBees. Financial AI Applications: Investment-related AI, financial data analysis. Contributions to Python open-source projects or packages. LLM Understanding & Safety: Transformers, attention mechanisms, fine-tuning, hallucination mitigation, AI safety guardrails. **Will accept any suitable combination of education, training, and experience.

Must possess unrestricted right to work in the U.S. in this position

Base Salary Compensation: $153,317.00

Morningstar is an equal opportunity employer.

Compensation and Benefits

At Morningstar we believe people are at their best when they are at their healthiest. That's why we champion your wellness through a wide range of programs that support all stages of your personal and professional life. Here are some examples of the offerings we provide:

  • Financial Health

    • 100% 401k match up to 6% of salary

    • Stock Ownership Potential

    • Company provided life insurance - 1x salary + commission

  • Physical Health

    • Comprehensive health benefits(medical/dental/vision)including potential premium discounts and company-provided HSA contributions (up to $500-$2,000 annually) for specific plansand coverages

    • Additional medical Wellness Incentives - up to $300-$600 annual

    • Company-provided long- and short-termdisabilityinsurance

  • Emotional Health

    • Trust-Based Time Off

    • 6-week Paid Sabbatical Program

    • 6-Week Paid Family Caregiving Leave

    • Competitive 8-24 Week Paid Parental Leave

    • Adoption Assistance

    • Leadership Coaching & FormalMentorshipOpportunities

    • Annual Flex Stipend - $1000 annually to cover personal education & well-being expenses

    • Tuition Reimbursement

  • Social Health

    • Charitable Matching Gifts program

    • Dollars for Doers volunteer program

    • Paid volunteering days

    • 15+ Employee Resource & Affinity Groups

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.

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