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Senior Llm Developer Jobs in Indiana (NOW HIRING)

Onebridge

Senior AI Full Stack Engineer

Indianapolis, IN · On-site

$120 - $180/hr

Build and integrate Generative AI capabilities using LLM platforms such as OpenAI, Azure OpenAI ... Experience with modern DevOps and cloud-native development, including containerization, CI/CD ...

Elevance Health

AI DevSecOps Senior Engineer

Indianapolis, IN · Hybrid

$109K - $150K/yr

... developer velocity. How you will make an Impact: * Define and implement secure SDLC practices ... Experience securing AI/LLM-enabled applications or AI-assisted development workflows * Familiarity ...

Elevance Health

AI DevSecOps Senior Engineer

Indianapolis, IN · On-site

$109K - $150K/yr

... developer velocity. How you will make an Impact: * Define and implement secure SDLC practices ... Experience securing AI/LLM-enabled applications or AI-assisted development workflows * Familiarity ...

BSA Lifestructures Inc

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, ...

Elevance Health

AI DevSecOps Senior Engineer

Indianapolis, IN · Hybrid

$109K - $150K/yr

Experience securing AI/LLM-enabled applications or AI-assisted development workflows * Familiarity ... Strong understanding of DevOps and Agile practices Please be advised that Elevance Health only ...

inKind

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 ...

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Senior Llm Developer information

What is a senior LLM developer?

Senior LLM Developers are experienced software engineers who specialize in building, fine-tuning, and deploying large language models (LLMs) such as GPT, BERT, or similar AI models. They work on advanced natural language processing (NLP) tasks, optimize model performance, and often lead teams in developing AI-driven applications. Their responsibilities include data pipeline development, model training, performance evaluation, and integrating LLMs into products. They are proficient in programming languages like Python, familiar with machine learning frameworks, and stay updated with the latest research in AI and NLP.

What are the key skills and qualifications needed to thrive as a senior LLM developer?

To thrive as a Senior LLM Developer, you need deep expertise in natural language processing, machine learning, and advanced programming skills, typically supported by a relevant degree and experience with large language models. Proficiency with frameworks like PyTorch or TensorFlow, cloud platforms (AWS, GCP, Azure), and version control systems, as well as familiarity with model fine-tuning and deployment, are essential. Strong problem-solving, communication, and collaboration skills help in leading teams and translating complex requirements into innovative solutions. These skills are crucial for building, optimizing, and maintaining robust language models that meet organizational objectives and stay ahead in a rapidly evolving field.

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

Senior LLM Developers often encounter challenges such as optimizing model performance for latency and scalability while maintaining accuracy. Managing resource-intensive inference and ensuring robust monitoring to detect issues like model drift or biased outputs are also key concerns. Additionally, integrating LLMs with existing systems and coordinating with cross-functional teams, such as MLOps engineers and product managers, is essential for successful deployment. Staying updated with rapidly evolving frameworks and compliance requirements adds to the complexity of the role.

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

AspectSenior Llm DeveloperMachine Learning Engineer
CredentialsAdvanced degrees in CS, NLP, or AI; experience with LLMsDegrees in CS, Data Science, or related fields; experience with ML frameworks
Work EnvironmentResearch labs, AI startups, tech companies focusing on NLPTech companies, startups, industries applying ML solutions
Industry UsagePrimarily in NLP, AI research, and language model developmentBroader across AI applications, including vision, speech, and data analysis

While both roles require strong AI and ML knowledge, Senior Llm Developers specialize in language models and NLP, whereas Machine Learning Engineers work across various AI domains. The roles often overlap but differ in focus and application areas.

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

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

What job categories do people searching Senior Llm Developer jobs in Indiana look for?

The top searched job categories for Senior Llm Developer jobs in Indiana are:

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

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

Senior Llm Developer jobs near you
Infographic showing various Senior Llm Developer job openings in Indiana as of August 2026, with employment types broken down into 78% Full Time, 7% Part Time, and 15% Contract. Highlights an 79% Physical, 7% Hybrid, and 14% Remote job distribution.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Indianapolis, IN • On-site

$140 - $210/hr

Other

Re-posted 11 days ago

Job description

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

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

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

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