Senior AI / Machine Learning Engineer

Qualifications

• Strong background in Applied Science, Data Science, Machine Learning, or AI Engineering

• Hands‑on experience with:

• LLMs (enterprise-grade)

• RAG architectures

• Prompt engineering

• Agentic AI workflows

• Experience building scalable AI systems and integrating them into real-world workflows

• Ability to explain complex AI concepts clearly to executives, engineers, and non‑technical stakeholders

• Strong presentation and client‑facing communication skills

• High executive visibility and the chance to influence AI strategy

• Short introductory conversation

• 8 more items(s)

Responsibilities

• This role focuses primarily on applied AI/LLM system development, while also leveraging traditional data science and machine learning skills to support broader modeling and analytics needs

• Design, build, and deploy enterprise-grade LLM applications using modern foundation models

• Develop RAG pipelines, vector-based retrieval, and contextual knowledge bases

• Engineer agentic AI workflows, multi-step reasoning chains, and autonomous decision systems

• Perform prompt engineering, optimization, evaluation, and guardrail construction

• Identify and mitigate hallucinations, design safety layers, and tune system reliability

• Build scalable, production-ready GenAI solutions leveraging AWS Bedrock and related services

• Collaborate with engineering and product teams to bring GenAI prototypes into full production

• 🔍 Data Scientist Responsibilities (Secondary but Required)

• Apply traditional ML techniques (classification, regression, clustering, etc.) when GenAI is not the right fit

• Conduct data exploration, feature engineering, and statistical analysis to support use‑case discovery

• Build, tune, and validate predictive models using established ML frameworks

• Analyze system performance, model outputs, and user interaction patterns

• Translate data insights into actionable recommendations for leadership and clients

• Support experimentation, A/B testing, and model performance measurement

• These responsibilities ensure a well-rounded individual capable of bridging both emerging GenAI technologies and core data science fundamentals

• Solid grounding in traditional ML (Python, scikit‑learn, data wrangling, evaluation metrics)

• Build solutions that go beyond prototypes into production AI systems that matter

• Opportunity to work across both GenAI and traditional ML, giving variety and depth to your work

• 16 more items(s)

More job highlights

Job description

Only accepting local Seattle, WA candidates

We are seeking an Applied Scientist / Data Scientist with strong expertise in Generative AI, LLMs, RAG architectures, and AWS Bedrock to build next‑generation AI capabilities for enterprise‑scale use cases. This role focuses primarily on applied AI/LLM system development, while also leveraging traditional data science and machine learning skills to supp...ort broader modeling and analytics needs.

This position requires 5 days/week on‑site in Seattle, WA and the ability to interview in person on short notice.

⭐ What You’ll Do (Applied Scientist Focus)

• Design, build, and deploy enterprise-grade LLM applications using modern foundation models.

• Develop RAG pipelines, vector-based retrieval, and contextual knowledge bases.

• Engineer agentic AI workflows, multi-step reasoning chains, and autonomous decision systems.

• Perform prompt engineering, optimization, evaluation, and guardrail construction.

• Identify and mitigate hallucinations, design safety layers, and tune system reliability.

• Build scalable, production-ready GenAI solutions leveraging AWS Bedrock and related services.

• Collaborate with engineering and product teams to bring GenAI prototypes into full production.

🔍 Data Scientist Responsibilities (Secondary but Required)

• Apply traditional ML techniques (classification, regression, clustering, etc.) when GenAI is not the right fit.

• Conduct data exploration, feature engineering, and statistical analysis to support use‑case discovery.

• Build, tune, and validate predictive models using established ML frameworks.

• Analyze system performance, model outputs, and user interaction patterns.

• Translate data insights into actionable recommendations for leadership and clients.

• Support experimentation, A/B testing, and model performance measurement.

These responsibilities ensure a well-rounded individual capable of bridging both emerging GenAI technologies and core data science fundamentals.

✔️ What You Bring

Technical Expertise:

• Strong background in Applied Science, Data Science, Machine Learning, or AI Engineering.

• Hands‑on experience with:

• LLMs (enterprise-grade)

• AWS Bedrock

• RAG architectures

• Prompt engineering

• Agentic AI workflows

• Solid grounding in traditional ML (Python, scikit‑learn, data wrangling, evaluation metrics).

• Experience building scalable AI systems and integrating them into real-world workflows.

Communication Skills:

• Ability to explain complex AI concepts clearly to executives, engineers, and non‑technical stakeholders.

• Strong presentation and client‑facing communication skills.

🎯 Why This Role Is Unique

• You’ll work at the intersection of research-level innovation and real enterprise deployment.

• Build solutions that go beyond prototypes into production AI systems that matter.

• High executive visibility and the chance to influence AI strategy.

• Opportunity to work across both GenAI and traditional ML, giving variety and depth to your work.

🧪 Interview Process

• Short introductory conversation

• 60-minute technical interview (LLMs, RAG, GenAI reasoning, and ML fundamentals)

• Client virtual interview, followed by an in‑person meeting for finalists

🌟 If you’re an Applied Scientist with strong Data Science fundamentals and a passion for building real GenAI solutions, this is an opportunity to help shape the next wave of enterprise AI.

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