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Senior Llm Engineer Jobs in Indiana (NOW HIRING)

Senior AI Full Stack Engineer | About You As a Senior AI Full Stack Engineer, you are responsible ... Build and integrate Generative AI capabilities using LLM platforms such as OpenAI, Azure OpenAI ...

Senior AI Full Stack Engineer | About You As a Senior AI Full Stack Engineer, you are responsible ... Build and integrate Generative AI capabilities using LLM platforms such as OpenAI, Azure OpenAI ...

Senior AI Full Stack Engineer | About You As a Senior AI Full Stack Engineer, you are responsible ... Build and integrate Generative AI capabilities using LLM platforms such as OpenAI, Azure OpenAI ...

Senior Software Engineer

Indianapolis, IN · On-site

$117K - $154K/yr

We're looking for a Senior Software Engineer to focus on building the core of our applications ... Demonstrated experience shipping LLM-powered applications to production(retrieval pipelines ...

Senior Mechanical Engineer

Carmel, IN · On-site

$99K - $130K/yr

We are searching for a Senior Mechanical Engineer to join our Indianapolis studio! What You'll Do ... Large Language Models (LLM's), familiarity with Deltek Vantagepoint preferred; Architecture CAD, ...

Data Engineer

Austin, IN

$135K - $155K/yr

Senior Data Engineer Role Summary: Data Engineering is a key role in the development team and is ... Proficiency in Python and modern AI/ML tooling and experience integrating with LLM APIs (Anthropic ...

Senior Software Engineer

Indianapolis, IN · On-site

$117K - $154K/yr

Senior Software Engineer Department & Team : Genesys Cloud CX and Core Services, Core AI Team Job ... test their LLM powered features, integrate with AWS Bedrock, and provide enterprise-grade ...

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Senior Llm Engineer information

What are the key skills and qualifications needed to thrive as a senior LLM engineer, and why are they important?

To thrive as a Senior LLM Engineer, you need deep expertise in machine learning, natural language processing, and advanced programming skills, typically supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as PyTorch, TensorFlow, Hugging Face Transformers, and cloud platforms, along with experience in deploying large-scale language models, is crucial. Strong problem-solving, collaboration, and communication skills set top performers apart in leading cross-functional AI initiatives. These abilities are vital for developing, optimizing, and scaling cutting-edge language models that drive innovation and business value.

Are senior Llm engineers in demand?

Senior Llm engineers are in high demand due to the rapid growth of artificial intelligence and natural language processing industries. They are sought after for their expertise in developing and deploying large language models, often requiring skills in machine learning frameworks, programming, and data management. This demand is expected to continue as AI applications expand across various sectors.

What is a senior LLM engineer?

Senior LLM (Large Language Model) Engineers are experienced professionals who design, build, optimize, and maintain advanced language models like GPT, BERT, or similar AI systems. They work on tasks such as model training, fine-tuning, deployment, and troubleshooting, often collaborating with data scientists and software engineers. Their expertise includes deep learning frameworks, natural language processing, and software engineering best practices. Senior LLM Engineers also play a key role in ensuring the ethical and efficient use of AI models in production systems.

What are some common challenges senior LLM engineers face when deploying large language models in production environments?

Senior LLM Engineers often encounter challenges related to scaling models efficiently, managing latency, and ensuring model outputs are safe and reliable. Deploying large language models requires careful optimization to balance performance with computational costs, as well as robust monitoring to detect and mitigate issues like bias or hallucination in outputs. Collaboration with cross-functional teams, including data scientists, product managers, and DevOps, is key to addressing these challenges and ensuring successful model deployment and maintenance.

What is the difference between Senior Llm Engineer vs Machine Learning Engineer?

AspectSenior Llm EngineerMachine Learning Engineer
CredentialsAdvanced degrees in CS, NLP, or AI; experience with LLMsDegrees in CS, Data Science, or AI; strong programming skills
Work EnvironmentFocus on NLP, language models, and large-scale data processingBroader ML tasks, including data modeling, algorithms, and deployment
Industry UsagePrimarily in AI/NLP-focused companies, research labs

Senior Llm Engineers specialize in large language models and NLP-specific tasks, often requiring advanced NLP knowledge and experience with LLMs. Machine Learning Engineers have a broader scope, working on various ML models and applications across industries. While both roles require strong technical skills, Senior Llm Engineers focus more on language-specific AI, whereas Machine Learning Engineers handle diverse ML projects.

What are the most commonly searched types of Llm Engineer jobs in Indiana?

The most popular types of Llm Engineer jobs in Indiana are:

What are popular job titles related to Senior Llm Engineer jobs in Indiana?

For Senior Llm Engineer jobs in Indiana, the most frequently searched job titles are:

What cities in Indiana are hiring for Senior Llm Engineer jobs?

Cities in Indiana with the most Senior Llm Engineer job openings:

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Indianapolis, IN • Remote

$117K - $154K/yr

Full-time

Re-posted 13 hours ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health · Remote (US) · Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.