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E2E AI SME Engineer-cum-Product Owner

Headway Tek Inc

Santa Ana, California • Today

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$80 - $88

AI / ML Engineer

Rresolveexpert Solutions LLC

Remote or Irvine, California • 29d ago

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Hyundai AutoEver America

Irvine, California • Today

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USD 130,510.00 - 172,000.00 per year

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Compensation information provided in the description

Senior Manager AI Engineer

CVS Health

Remote or New York, New York • Today

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USD 106,605.00 - 260,590.00 per year

Junior AI Engineer (0-5 Years)

Pioneer Consulting Services LLC

Remote • 2d ago

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Senior ML Engineer, Core Development

Anduril Industries

Costa Mesa, California • 7d ago

Full-time

USD 220,000.00 - 292,000.00 per year

Manager - Machine Learning & Artificial Intelligence

Glidewell Dental

Irvine, California • Today

Full-time

USD 152,000.00 - 200,000.00 per year

Software Engineer/Developer (AI Focus) -

Pyramid Systems, Inc.

Remote • 3d ago

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Senior AI/ML Consultant

ScaleHealthTech LLC

Remote • Today

Easy Apply

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Senior AI Developer (Palantir AI Platform)

Vaco by Highspring

Remote • 15d ago

Easy Apply

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Senior AI/ML Applied Scientist - Remote

UnitedHealth Group

Remote or Bellevue, Washington • Today

Full-time

USD 130,000.00 - 162,500.00 per year

Gen AI/ML Engineer - Entry Level

NET2ASPIRE LLC

Remote • 15d ago

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33 - 40

AI Field Engineer - Enterprise

Fireworks AI

Remote or New York, New York • Today

Full-time

USD 200,000.00 - 260,000.00 per year

Sr Generative AI Scientist II - (Model Risk)

Cotiviti

Remote • Today

Full-time

USD 153,000.00 - 180,000.00 per year

Full stack Ai Engineer-AI / ML Implementation, Python, TensorFlow, AI ML with Claude

Datum Software, Inc.

Remote • 7d ago

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Principal Data Scientist - Remote

UnitedHealth Group

Remote or Minnetonka, Minnesota • Today

Full-time

USD 112,700.00 - 193,200.00 per year

AI/ML Engineer

Medinext Global LLC

Remote • Yesterday

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Lead AI/ML Applied Scientist - Remote

UnitedHealth Group

Remote or Bellevue, Washington • Today

Full-time

USD 154,900.00 - 193,600.00 per year

Senior Machine Learning Engineer, Applied Intelligence

Anduril Industries

Santa Ana, California • 7d ago

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USD 220,000.00 - 292,000.00 per year

AI/ML Engineering Lead

Protective

Remote or Nebraska • Today

Full-time

USD 124,500.00 - 180,000.00 per year

AI/ML Engineer

Slate Auto

Remote or Washington • Today

Full-time

Sr AI/ML Engineer Remote Nationwide or Office-Based in MN/DC

UnitedHealth Group

Remote or Minnetonka, Minnesota • Today

Full-time

USD 120,100.00 - 214,500.00 per year

Data Scientist - Geospatial and Remote Sensing (Summer Internship - Master's)

Balyasny Asset Management

Remote or New York, New York • Today

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Staff Applied AI Scientist

Lumen

Remote or Oregon • Today

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USD 152,066.00 - 253,444.00 per year

Senior AI/ML Engineer - Remote

UnitedHealth Group

Remote or Eden Prairie, Minnesota • Today

Full-time

USD 120,100.00 - 214,500.00 per year

Member of Technical Staff, Agentic AI Architect

Micron Technology, Inc.

Remote or Idaho • Today

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Senior Machine Learning Engineer

Odyssey Systems Consulting Group, Ltd.

Remote • Today

Full-time

USD 165,000.00 - 195,000.00 per year

Job Details

Skills

Generative Artificial Intelligence (AI)
Large Language Models (LLMs)
Product Strategy
Business Cases
Continuous Improvement
Business Data
Legal
Functional Requirements
Sprint
Use Cases
Prototyping
Data Modeling
KPI
Optimization
Development Testing
Automated Testing
Incident Management
Root Cause Analysis
Service Level
Access Control
Change Management
Training
Mentorship
Computer Science
Data Science
Software Design
Workflow
Prompt Engineering
Product Ownership
Management
Roadmaps
User Stories
Agile
Cloud Computing
Python
SQL
Testing
Git
Machine Learning (ML)
PyTorch
TensorFlow
LangChain
LlamaIndex
Vector Databases
Search Technologies
Orchestration
Unstructured Data
Amazon Web Services
Microsoft Azure
Google Cloud
Google Cloud Platform
Kubernetes
Continuous Integration
Continuous Delivery
Machine Learning Operations (ML Ops)
Privacy
Artificial Intelligence
Evaluation
Performance Tuning
Product Requirements
Solution Architecture
Communication
Facilitation
Analytical Skill
Decision-making
Leadership
Collaboration

Generative Artificial Intelligence (AI)
Large Language Models (LLMs)
Product Strategy
Business Cases
Continuous Improvement
Business Data
Legal
Functional Requirements
Sprint
Use Cases
Prototyping
Data Modeling
KPI
Optimization
Development Testing
Automated Testing
Incident Management
Root Cause Analysis
Service Level
Access Control
Change Management
Training
Mentorship
Computer Science
Data Science
Software Design
Workflow
Prompt Engineering
Product Ownership
Management
Roadmaps
User Stories
Agile
Cloud Computing
Python
SQL
Testing
Git
Machine Learning (ML)
PyTorch
TensorFlow
LangChain
LlamaIndex
Vector Databases
Search Technologies
Orchestration
Unstructured Data
Amazon Web Services
Microsoft Azure
Google Cloud
Google Cloud Platform
Kubernetes
Continuous Integration
Continuous Delivery
Machine Learning Operations (ML Ops)
Privacy
Artificial Intelligence
Evaluation
Performance Tuning
Product Requirements
Solution Architecture
Communication
Facilitation
Analytical Skill
Decision-making
Leadership
Collaboration

Summary

E2E AI SME Engineer-cum-Product Owner

Location: Santa Ana, California (Onsite)

Work profile: Hands-on engineering and product ownership

C2C or W2

Job Description:

Experienced, hands-on E2E AI Subject Matter Expert (SME) Engineer-cum-Product Owner to own the strategy, design, development, deployment, and continuous improvement of enterprise AI products. The ideal candidate combines deep expertise in machine learning, generative AI, large language models (LLMs), agentic AI, and retrieval-augmented generation (RAG) with strong product leadership. This role will translate business needs into a prioritized product roadmap, build production-grade solutions, and drive measurable adoption and business value from discovery through operations.

Key Responsibilities:

Product Strategy & Ownership:
- Own the end-to-end AI product lifecycle, including discovery, vision, business case, roadmap, backlog, delivery, launch, adoption, and continuous improvement.
- Partner with business, data, engineering, security, legal, and operations stakeholders to define user journeys, functional and non-functional requirements, acceptance criteria, and measurable outcomes.
- Prioritize features and technical enablers based on customer value, feasibility, risk, cost, and strategic alignment; lead sprint planning, reviews, and release readiness.
Hands-on AI Engineering:
- Design and build production-grade AI applications using Python, APIs, LLM frameworks, vector databases, and cloud-native services.
- Develop LLM, RAG, agentic AI, fine-tuning, prompt engineering, and tool-calling solutions for enterprise use cases.
- Create reference architectures, reusable components, technical prototypes, and coding standards while contributing directly to critical implementation work.
Data, Model & Evaluation:
- Define data ingestion, chunking, embedding, retrieval, orchestration, and model-selection approaches across structured and unstructured data.
- Establish offline and online evaluation frameworks covering quality, relevance, groundedness, hallucination, latency, cost, safety, and business KPIs.
- Lead experimentation, model comparison, prompt and retrieval optimization, red-teaming, and human-in-the-loop validation.
Deployment, MLOps/LLMOps & Operations:
- Lead secure deployment of AI services across development, test, and production environments using CI/CD, containers, infrastructure as code, and automated testing.
- Implement observability for model and application performance, data drift, prompt and response traces, token usage, latency, reliability, and cost.
- Own production readiness, incident response, root-cause analysis, release governance, vendor coordination, and service-level objectives.
Responsible AI, Security & Governance:
- Embed privacy, security, responsible AI, explainability, access control, auditability, and regulatory requirements throughout the product lifecycle.
- Identify and mitigate risks related to sensitive data, prompt injection, data leakage, model misuse, bias, harmful output, and third-party models.
Stakeholder Leadership & Adoption:
- Communicate product decisions, trade-offs, architecture, progress, risks, and value realization to technical and executive audiences.
- Drive pilots, user feedback, change management, training, adoption metrics, and scale-up plans; mentor engineers and promote AI engineering best practices.

Key Requirements:

Education:
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI/ML, or a related field, or equivalent practical experience.
Experience:
- Proven experience delivering enterprise AI/ML products end to end, with direct ownership from problem discovery and solution design through production deployment and adoption.
- Strong hands-on experience building applications with LLMs, RAG, agentic workflows, prompt engineering, embeddings, vector search, and model evaluation.
- Demonstrated product ownership experience managing roadmaps, backlogs, user stories, acceptance criteria, stakeholder expectations, and Agile delivery.
- Experience integrating AI solutions with enterprise data platforms, APIs, business applications, and cloud ecosystems.
- Track record of deploying, monitoring, troubleshooting, and optimizing secure, scalable AI solutions in production.
Technical Skills:
- Advanced Python and SQL skills, with experience in service development, REST APIs, testing, Git, and production-grade coding practices.
- Proficiency with AI/ML frameworks and LLM application tooling such as PyTorch or TensorFlow, Hugging Face, LangChain or LlamaIndex, and model-provider SDKs.
- Experience with vector databases, search technologies, data pipelines, orchestration, and structured and unstructured data processing.
- Working knowledge of AWS, Azure, or Google Cloud Platform; containers and Kubernetes; CI/CD; MLOps/LLMOps; observability; and infrastructure-as-code practices.
- Strong understanding of AI security, privacy, responsible AI, model risk, evaluation methods, and cost-performance optimization.
Product & Leadership Skills:
- Ability to convert ambiguous business problems into clear product requirements, solution architecture, prioritized delivery plans, and measurable outcomes.
- Excellent communication, facilitation, analytical thinking, customer empathy, decision-making, and cross-functional leadership skills.
- Ability to work hands-on while guiding multidisciplinary teams and influencing senior stakeholders without relying solely on formal authority.
Location:
- Based in or able to work from Santa Ana, California, with availability for in-person collaboration as required.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91112461
Position Id: OOJ - 3524-2525-1792772623
Posted 1 hour ago

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Job Details

Skills

Generative Artificial Intelligence (AI)
Large Language Models (LLMs)
Product Strategy
Business Cases
Continuous Improvement
Business Data
Legal
Functional Requirements
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Data Modeling
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Legal
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Summary

E2E AI SME Engineer-cum-Product Owner

Location: Santa Ana, California (Onsite)

Work profile: Hands-on engineering and product ownership

C2C or W2

Job Description:

Key Responsibilities:

Product Strategy & Ownership:
- Own the end-to-end AI product lifecycle, including discovery, vision, business case, roadmap, backlog, delivery, launch, adoption, and continuous improvement.
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Hands-on AI Engineering:
- Design and build production-grade AI applications using Python, APIs, LLM frameworks, vector databases, and cloud-native services.
- Develop LLM, RAG, agentic AI, fine-tuning, prompt engineering, and tool-calling solutions for enterprise use cases.
- Create reference architectures, reusable components, technical prototypes, and coding standards while contributing directly to critical implementation work.
Data, Model & Evaluation:
- Define data ingestion, chunking, embedding, retrieval, orchestration, and model-selection approaches across structured and unstructured data.
- Establish offline and online evaluation frameworks covering quality, relevance, groundedness, hallucination, latency, cost, safety, and business KPIs.
- Lead experimentation, model comparison, prompt and retrieval optimization, red-teaming, and human-in-the-loop validation.
Deployment, MLOps/LLMOps & Operations:
- Lead secure deployment of AI services across development, test, and production environments using CI/CD, containers, infrastructure as code, and automated testing.
- Implement observability for model and application performance, data drift, prompt and response traces, token usage, latency, reliability, and cost.
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Stakeholder Leadership & Adoption:
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Key Requirements:

Education:
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Experience:
- Proven experience delivering enterprise AI/ML products end to end, with direct ownership from problem discovery and solution design through production deployment and adoption.
- Strong hands-on experience building applications with LLMs, RAG, agentic workflows, prompt engineering, embeddings, vector search, and model evaluation.
- Demonstrated product ownership experience managing roadmaps, backlogs, user stories, acceptance criteria, stakeholder expectations, and Agile delivery.
- Experience integrating AI solutions with enterprise data platforms, APIs, business applications, and cloud ecosystems.
- Track record of deploying, monitoring, troubleshooting, and optimizing secure, scalable AI solutions in production.
Technical Skills:
- Advanced Python and SQL skills, with experience in service development, REST APIs, testing, Git, and production-grade coding practices.
- Proficiency with AI/ML frameworks and LLM application tooling such as PyTorch or TensorFlow, Hugging Face, LangChain or LlamaIndex, and model-provider SDKs.
- Experience with vector databases, search technologies, data pipelines, orchestration, and structured and unstructured data processing.
- Working knowledge of AWS, Azure, or Google Cloud Platform; containers and Kubernetes; CI/CD; MLOps/LLMOps; observability; and infrastructure-as-code practices.
- Strong understanding of AI security, privacy, responsible AI, model risk, evaluation methods, and cost-performance optimization.
Product & Leadership Skills:
- Ability to convert ambiguous business problems into clear product requirements, solution architecture, prioritized delivery plans, and measurable outcomes.
- Excellent communication, facilitation, analytical thinking, customer empathy, decision-making, and cross-functional leadership skills.
- Ability to work hands-on while guiding multidisciplinary teams and influencing senior stakeholders without relying solely on formal authority.
Location:
- Based in or able to work from Santa Ana, California, with availability for in-person collaboration as required.

Dice Id: 91112461
Position Id: OOJ - 3524-2525-1792772623
Posted 1 hour ago

Fitment

Dice Job Match Score™

To see how well you match this job, please log in or create an account.

Once logged in, be sure to complete your profile to get the most accurate match score.

Create job alert

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