Llm Developer Jobs in Raleigh, NC (NOW HIRING)

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.

Work you'll do

As an Agentic AI FDE, you will design, build, and operationalize LLM-powered systems that can reason, plan, retrieve information, use tools, and execute multi-step workflows reliably. You will work on the "thinking layer" of AI systems: agent architecture, tool orchestration, memory and context management, retrieval pipelines, evaluation, and observability. You will help shape how complex domain knowledge is transformed into usable AI behavior, with a high bar for precision, traceability, and maintainability. Additional responsibilities include:

Client Engagement

Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.

Partner with leaders, product owners, architects, and engineers to align priorities and delivery.

Lead working sessions to shape solutions and drive client outcomes.

Prototype and deliver working AI solutions using industry expertise and emerging capabilities.

Contribute independently within an FDE pod while mentoring newer team members.

Solution Engineering

Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.

Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.

Apply architecture decisions that balance quality, safety, latency, cost, and model risk.

Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.

Design extensible functionality, support sprint sizing, and align solutions with senior team members.

Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.

Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal token selection.

Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade-offs, and evaluation methods. 

Experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

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.

3+ years of experience in software engineering, data engineering, data science, or analytics engineering.

1+ years of hands-on experience building production-grade applications with LLMs, including prompt design, tool use, structured outputs, error handling, and model behavior tuning.

1+ years of experience with LangChain and especially LangGraph for orchestrating complex LLM workflows and agent behavior.

1+ years of experience designing and optimizing RAG systems end to end, including indexing, retrieval, reranking, grounding, and evaluation.

1+ years of experience with memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal token selection.

Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade-offs, and evaluation methods.

2+ years of experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments

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

Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal token selection. 

Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade-offs, and evaluation methods. 

Experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

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 $134,500 to $265,100.

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.

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.

Work you'll do

As an Agentic AI FDE, you will design, build, and operationalize LLM-powered systems that can reason, plan, retrieve information, use tools, and execute multi-step workflows reliably. You will work on the "thinking layer" of AI systems: agent architecture, tool orchestration, memory and context management, retrieval pipelines, evaluation, and observability. You will help shape how complex domain knowledge is transformed into usable AI behavior, with a high bar for precision, traceability, and maintainability. Additional responsibilities include:

Client Engagement

Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.

Partner with leaders, product owners, architects, and engineers to align priorities and delivery.

Lead working sessions to shape solutions and drive client outcomes.

Prototype and deliver working AI solutions using industry expertise and emerging capabilities.

Contribute independently within an FDE pod while mentoring newer team members.

Solution Engineering

Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.

Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.

Apply architecture decisions that balance quality, safety, latency, cost, and model risk.

Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.

Design extensible functionality, support sprint sizing, and align solutions with senior team members.

Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.

Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal token selection.

Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade-offs, and evaluation methods. 

Experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

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.

3+ years of experience in software engineering, data engineering, data science, or analytics engineering.

1+ years of hands-on experience building production-grade applications with LLMs, including prompt design, tool use, structured outputs, error handling, and model behavior tuning.

1+ years of experience with LangChain and especially LangGraph for orchestrating complex LLM workflows and agent behavior.

1+ years of experience designing and optimizing RAG systems end to end, including indexing, retrieval, reranking, grounding, and evaluation.

1+ years of experience with memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal token selection.

Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade-offs, and evaluation methods.

2+ years of experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments

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

Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal token selection. 

Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade-offs, and evaluation methods. 

Experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

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 $134,500 to $265,100.

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.