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Llm Ml Rag Jobs in Indiana (NOW HIRING)

Performacentric

ML Engineer

Indianapolis, IN · On-site +1

... LLM performance for business use cases. * Implement Retrieval-Augmented Generation (RAG ... ML Engineering and MLOps practices. * LangChain, LlamaIndex, Haystack, or similar frameworks.

Liberty Mutual

Senior Software Engineer

Indianapolis, IN · Hybrid

$117K - $154K/yr

... AI/ML (Working Knowledge) | RAG, chunking, embeddings, LLM summarization | | Security & Access | IAM, RBAC, SSO, OAuth/SAML, access automation | | Operations | Incident management, RCA, ITIL ...

Liberty Mutual

Senior Software Engineer

Indianapolis, IN · Hybrid

$117K - $154K/yr

... AI/ML (Working Knowledge) | RAG, chunking, embeddings, LLM summarization | | Security & Access | IAM, RBAC, SSO, OAuth/SAML, access automation | | Operations | Incident management, RCA, ITIL ...

inKind

Data Engineer

Austin, IN

$135K - $155K/yr

... generation (RAG) systems. Responsibilities: * Responsible for the design, deployment, and ... Proficiency in Python and modern AI/ML tooling and experience integrating with LLM APIs (Anthropic ...

Hearst

AI Product Architect Director

Indianapolis, IN · On-site

Minimum Qualifications * 10+ years in engineering, technical architecture, AI/ML systems, or related fields. * 3+ years hands-on experience building or deploying GenAI, LLM, RAG, or agent-based ...

ZYLO, INC.

Sr. AI Engineer

Indianapolis, IN · On-site

$99K - $137K/yr

Build and implement RAG (Retrieval-Augmented Generation) systems to dramatically improve AI ... Design and optimize schemas for storing LLM interactions, agent state, and conversation history ...

ZYLO, INC.

Sr. AI Engineer

Indianapolis, IN · On-site

$99K - $137K/yr

Build and implement RAG (Retrieval-Augmented Generation) systems to dramatically improve AI ... Design and optimize schemas for storing LLM interactions, agent state, and conversation history ...

Zylo

Sr. AI Engineer

Indianapolis, IN · On-site

$99K - $137K/yr

Build and implement RAG (Retrieval-Augmented Generation) systems to dramatically improve AI ... Design and optimize schemas for storing LLM interactions, agent state, and conversation history ...

Deloitte

Google AI Lead Architect

Indianapolis, IN

$52.75 - $72.50/hr

LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models ... RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability). * Deep ...

Coforge

AI Tech Lead

Indianapolis, IN · On-site

AI/ML solutions, especially AI Agents or LLM-based systems. * Frameworks such as LangChain, OpenAI ... Familiarity with vector databases, embeddings, and RAG architecture. * Knowledge of DevOps ...

Allied Solutions LLC

Senior AI Architect

Carmel, IN · On-site

Responsibilities : • Architect end-to-end AI/ML systems including data pipelines, feature stores ... experience with LLM deployment, vector databases, RAG architecture, or similar emerging AI ...

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All JobsLlm Ml Rag JobsLlm Ml Rag Jobs in Indiana

Llm Ml Rag information

What are some typical challenges faced when working on Retrieval-Augmented Generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as an LLM ML RAG (Retrieval-Augmented Generation) Engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What are LLM ML RAG jobs?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.
What are popular job titles related to Llm Ml Rag jobs in Indiana? For Llm Ml Rag jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Llm Ml Rag jobs in Indiana look for? The top searched job categories for Llm Ml Rag jobs in Indiana are:
What cities in Indiana are hiring for Llm Ml Rag jobs? Cities in Indiana with the most Llm Ml Rag job openings:
Llm Ml Rag jobs near you

ML Engineer

Performacentric

Indianapolis, IN • On-site, Remote

Full-time

Posted 26 days ago

Job description

Machine Learning Engineer (Llama AI Platform)

Location: Remote (Preferred U.S. Time Zones)
Employment Type: Full-Time
Company: Performacentric

About Performacentric

Performacentric helps small and mid-market organizations improve profitability, efficiency, visibility, employee performance, customer satisfaction, and supplier performance through custom AI agents, intelligent automation, and connected business systems.

We are building a next-generation AI platform powered by open-source large language models, agentic workflows, and business process automation. We are seeking a Machine Learning Engineer to help design, deploy, and optimize AI solutions built on Llama models and modern Python-based application architectures.

Position Summary

Performacentric is seeking a Machine Learning Engineer with hands-on experience developing and deploying AI applications using Llama 3 8B, Python, and FastAPI. This role will be responsible for building production-grade AI services, optimizing model performance, developing APIs, integrating business systems, and supporting the evolution of Performacentric's AI agent platform.

The ideal candidate combines strong software engineering skills with practical machine learning experience and enjoys working in a fast-paced startup environment where they can directly influence product direction and technical architecture.

ResponsibilitiesAI Model Development & Optimization
  • Deploy, configure, and optimize Llama 3 8B models for production use.
  • Develop prompt engineering, retrieval, and agentic workflows.
  • Fine-tune and evaluate LLM performance for business use cases.
  • Implement Retrieval-Augmented Generation (RAG) architectures.
  • Optimize inference performance, latency, and infrastructure utilization.
  • Monitor model quality and continuously improve response accuracy.
Application Development
  • Build scalable AI applications using Python and FastAPI.
  • Design and maintain RESTful APIs for AI services.
  • Develop backend services supporting AI agents and copilots.
  • Integrate AI solutions with CRM, ERP, communication, and business systems.
  • Implement authentication, authorization, and API security controls.
  • Write clean, maintainable, and well-documented code.
Data & Infrastructure
  • Build and maintain vector database integrations.
  • Develop data ingestion and preprocessing pipelines.
  • Support deployment of AI workloads in cloud and self-hosted environments.
  • Collaborate on model serving, monitoring, logging, and observability.
  • Assist with infrastructure automation and CI/CD processes.
Collaboration
  • Work closely with product, engineering, and leadership teams.
  • Participate in architecture discussions and technical planning.
  • Contribute to AI solution design for client implementations.
  • Mentor junior developers and share best practices.
Required QualificationsTechnical Skills
  • 3+ years of professional software engineering experience.
  • Strong proficiency in Python.
  • Experience building APIs with FastAPI.
  • Experience deploying and working with Llama 3 8B or similar open-source LLMs.
  • Understanding of prompt engineering and LLM optimization techniques.
  • Experience consuming and developing REST APIs.
  • Strong understanding of Git-based development workflows.
  • Familiarity with Linux environments and command-line tools.
  • Experience troubleshooting and optimizing production applications.
Machine Learning Knowledge
  • Understanding of machine learning fundamentals.
  • Experience evaluating AI model performance.
  • Familiarity with embeddings, vector search, and RAG architectures.
  • Knowledge of model inference optimization techniques.
  • Experience working with structured and unstructured datasets.
Preferred Qualifications

Preference will be given to candidates with experience in one or more of the following:

  • Fine-tuning open-source LLMs.
  • ML Engineering and MLOps practices.
  • LangChain, LlamaIndex, Haystack, or similar frameworks.
  • PostgreSQL database administration and optimization.
  • Vector databases such as pgvector, Chroma, Pinecone, Weaviate, or Qdrant.
  • Docker and containerized deployments.
  • Kubernetes orchestration.
  • Azure AI infrastructure and GPU environments.
  • CI/CD pipelines and DevOps automation.
  • Multi-agent AI architectures.
  • Knowledge graph implementations.
  • Business intelligence and analytics platforms.
Success Metrics

Within the first 12 months, the successful candidate will help:

  • Deploy and optimize production AI workloads.
  • Improve AI response quality and accuracy.
  • Reduce inference latency and infrastructure costs.
  • Expand Performacentric's AI agent platform capabilities.
  • Deliver reliable AI integrations for customer environments.
  • Contribute to the development of new AI-powered products and services.
What We Offer
  • Opportunity to work on cutting-edge AI and agentic technologies.
  • Direct influence on product architecture and technical strategy.
  • Remote-first work environment.
  • Competitive compensation based on experience.
  • Professional growth opportunities in one of the fastest-growing areas of software development.
  • Ability to help shape the future of AI-powered business transformation.
How to Apply

Interested candidates should submit:

  • Resume/CV
  • Brief cover letter
  • GitHub profile (if available)
  • Portfolio of AI, machine learning, or software development projects
  • Examples of LLM, FastAPI, or AI agent implementations (preferred)

Join Performacentric and help build the next generation of AI agents that transform how businesses operate, make decisions, and grow.