Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management * Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event ...
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management * Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event ...
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management * Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event ...
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management * Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event ...
Senior Forward Deployed Engineer- AWS
Portland, OR · On-site
$110K - $152K/yr
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management * Experience with SageMaker fine-tuning (SFT and RFT) for domain SLMs * Experience integrating ...
Senior Forward Deployed Engineer- AWS
Portland, OR · On-site
$110K - $152K/yr
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management * Experience with SageMaker fine-tuning (SFT and RFT) for domain SLMs * Experience integrating ...
Senior AI Engineer
Odell, OR · On-site
$107K - $147K/yr
Knowledge in Python and relevant ML libraries, out-of-the box LLMs, Generative AI solutions and LLMOps * Automation of ML workflows and pipelines * Experience in AI managed services like H20 ...
Senior AI Engineer
Odell, OR · On-site
$107K - $147K/yr
Knowledge in Python and relevant ML libraries, out-of-the box LLMs, Generative AI solutions and LLMOps * Automation of ML workflows and pipelines * Experience in AI managed services like H20 ...
Llmops information
What is the difference between Llmops vs Data Scientist?
| Aspect | Llmops | Data Scientist |
|---|---|---|
| Required credentials | Knowledge of machine learning, AI frameworks, cloud platforms | Statistics, programming, data analysis skills |
| Work environment | AI/ML teams, cloud environments, deployment pipelines | Data analysis, modeling, reporting in various industries |
| Employer usage | Tech companies, AI startups, research labs | Finance, healthcare, tech, retail |
While both roles involve working with data and machine learning, Llmops focuses on deploying and maintaining large language models in production environments, requiring expertise in AI infrastructure. Data Scientists primarily analyze data, build models, and generate insights. Llmops professionals ensure models operate efficiently at scale, whereas Data Scientists develop the models and interpret results.
What are popular job titles related to Llmops jobs in Oregon?
For Llmops jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Llmops jobs in Oregon look for?
The top searched job categories for Llmops jobs in Oregon are:
What cities in Oregon are hiring for Llmops jobs?
Cities in Oregon with the most Llmops job openings:

Deloitte rating
8.2
Based on 92 frontline employees who took The Breakroom Quiz
44th 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 Snowflake FDE, you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. 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.
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 and deploying GenAI/LLM-powered solutions in client or production environments
- 1+ years of experience with Snowflake including hands-on experience with one of the following key platforms; Cortex AI, Cortex LLM Functions, Cortex Agents, Arctic Embed
- 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 $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.
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 Snowflake FDE, you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. 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.
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 and deploying GenAI/LLM-powered solutions in client or production environments
- 1+ years of experience with Snowflake including hands-on experience with one of the following key platforms; Cortex AI, Cortex LLM Functions, Cortex Agents, Arctic Embed
- 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 $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.
Education:Bachelor's DegreeEmployment Type:About Deloitte
Sourced by ZipRecruiter
Industry
Finance and insurance
Company size
10,000+ Employees
Headquarters location
Orlando, FL, US