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Llm Prompt Engineer Jobs in Indiana (NOW HIRING)

Data Engineer

Austin, IN · On-site

$135K - $155K/yr

Proficiency in Python and modern AI/ML tooling and experience integrating with LLM APIs (Anthropic ... prompt injection and data leakage to third-party AI services) Benefits & Perks at inKind At inKind ...

AI Engineer

Carmel, IN · On-site

$152K - $177K/yr

Hands-on experience working with prompt engineering - building Retrieval-Augmented Generations (RAG ... Experience integrating AI/LLM tools such as OpenAI, Azure OpenAI, or Anthropic APIs into ...

Showing results 21-40

Llm Prompt Engineer information

What are some common challenges faced by LLM Prompt Engineers when designing effective prompts for large language models?

LLM Prompt Engineers often encounter challenges such as ensuring prompts are both clear and unambiguous to elicit accurate model responses, as well as avoiding bias or unintended outputs. Balancing creativity and specificity in prompt design can be tricky, especially when tailoring prompts for diverse user intents or specialized domains. Additionally, prompt engineers must frequently iterate and test their prompts, collaborating closely with data scientists and product teams to continually refine them based on observed model behavior and user feedback.

What is an LLM Prompt Engineer?

An LLM Prompt Engineer is a professional who specializes in designing, testing, and optimizing prompts for large language models (LLMs) such as GPT-4. Their role involves crafting effective instructions and queries to guide the model's output for specific applications, ensuring accuracy, relevance, and reliability. They may also analyze model behavior, implement prompt-based workflows, and collaborate with developers to integrate LLMs into products or services. The goal is to maximize the performance and efficiency of language models in various real-world contexts.

What are the key skills and qualifications needed to thrive as an LLM Prompt Engineer?

To thrive as an LLM Prompt Engineer, you need a deep understanding of natural language processing, prompt engineering strategies, and proficiency in programming languages such as Python, often supported by a degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), large language model APIs, and version control systems is typically required. Strong analytical thinking, creativity, and effective communication are crucial soft skills for crafting precise prompts and collaborating with cross-functional teams. These skills ensure the development of effective, ethical, and high-performing AI-powered solutions that meet diverse user needs.

What is the difference between Llm Prompt Engineer vs Data Scientist?

AspectLlm Prompt EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related fields; familiarity with NLP and AI toolsBachelor's or higher in CS, Statistics, or related fields; strong programming and statistical skills
Work EnvironmentAI labs, tech companies, startups focusing on NLP and AI modelsData analysis, modeling, and visualization in various industries like finance, healthcare, tech
Employer & Industry UsagePrimarily in AI development, NLP projects, and machine learning teamsAcross industries for data analysis, predictive modeling, and decision support

While both roles involve working with data and AI, Llm Prompt Engineers focus on designing prompts for language models, whereas Data Scientists analyze data to derive insights. The roles share similar educational backgrounds and work environments but differ in their core tasks and industry applications.

What are popular job titles related to Llm Prompt Engineer jobs in Indiana? For Llm Prompt Engineer jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Llm Prompt Engineer jobs in Indiana look for? The top searched job categories for Llm Prompt Engineer jobs in Indiana are:
What cities in Indiana are hiring for Llm Prompt Engineer jobs? Cities in Indiana with the most Llm Prompt Engineer job openings:

Lead Forward Deployed Engineer, Palantir

Deloitte

Indianapolis, IN

$98K - $129K/yr

Full-time

Re-posted 28 days ago


Deloitte rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

45th of 150 rated financial services


Job description

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

Recruiting for this role ends on 10/30/2026.

Work you'll do

As a Lead Palantir FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte's most strategic clients. You'll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You'll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale.

Client Engagement

  • Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
  • Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
  • Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
  • Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements-contributing to pipeline development and deal shaping.

Cross-Functional Pod Leadership & Program Governance

  • Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
  • Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
  • Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
  • Mentor and develop junior FDEs

GenAI Solution Development

  • Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
  • Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
  • Govern end-to-end RAG pipeline design-including ingestion, chunking, embedding, vector retrieval, and hybrid search-ensuring production-grade quality and scalability.
  • Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Engineering & Data Foundations

  • Review and contribute to production-quality code
  • Guide architecture of data pipelines powering GenAI use cases
  • Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
  • Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)


The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Required qualifications 

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering
  • 7+ years of experience in software engineering, data engineering, data science, or analytics engineering
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Palantir including hands-on experience with one of the following key platforms; Foundry, AIP, Maven 
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code 
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available

Preferred qualifications

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments 
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management 
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures 
  • Experience operating within hybrid onshore/offshore teams 
  • Familiarity with security, privacy, and compliance considerations

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $189,200 to $372,900.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Qualifications:

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

Recruiting for this role ends on 10/30/2026.

Work you'll do

As a Lead Palantir FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte's most strategic clients. You'll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You'll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale.

Client Engagement

  • Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
  • Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
  • Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
  • Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements-contributing to pipeline development and deal shaping.

Cross-Functional Pod Leadership & Program Governance

  • Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
  • Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
  • Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
  • Mentor and develop junior FDEs

GenAI Solution Development

  • Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
  • Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
  • Govern end-to-end RAG pipeline design-including ingestion, chunking, embedding, vector retrieval, and hybrid search-ensuring production-grade quality and scalability.
  • Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Engineering & Data Foundations

  • Review and contribute to production-quality code
  • Guide architecture of data pipelines powering GenAI use cases
  • Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
  • Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)


The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Required qualifications 

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering
  • 7+ years of experience in software engineering, data engineering, data science, or analytics engineering
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Palantir including hands-on experience with one of the following key platforms; Foundry, AIP, Maven 
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code 
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
  • Limited immigration sponsorship may be available

Preferred qualifications

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments 
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management 
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures 
  • Experience operating within hybrid onshore/offshore teams 
  • Familiarity with security, privacy, and compliance considerations

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $189,200 to $372,900.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Education:Bachelor's DegreeEmployment Type:

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