Context Engineering

Context Engineering Jobs

2 jobs found

1mo ago

AI Engineer

Hollstadt Consulting Minneapolis, MN 🇺🇸 Remote

Minneapolis, MN

1mo ago Closed

$105k - $140k/yr Full time AI Ai Engineer Ai Engineering

This position is no longer accepting applications (closed Sep 8, 2026).

Job Description

Hollstadt Consulting is building out its AI Center of Excellence and needs an AI/ML Engineer to support cross-functional teams with production-focused machine learning, AI, Generative AI, and agentic AI solutions. This role is centered on designing and delivering end-to-end capabilities, from pipeline development to testing and secure integration across enterprise environments. This position is remote in Minnesota, with local preference.

Key work includes building production AI/ML pipelines and GenAI or agent capabilities tailored to domain-specific needs. The role supports automation of complex document understanding and structured content extraction, while also establishing reliability measures such as LLM testing frameworks and regression suites for drift and prompt breakage.

Responsibilities

Collaborate across GenAI platforms and libraries, including AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms.
Automate workflows to extract highly accurate structured outputs from complex, multimodal unstructured content using tools such as AWS Textract and Bedrock.
Design and build Model Context Protocol (MCP) hosts, clients, and servers.
Establish and apply frameworks for automated LLM testing.
Create regression test suites to detect drift and prompt breakage.
Integrate with internal and external web services using secure authentication and authorization mechanisms.
Adopt safe practices to protect against prompt injections and jailbreaks, aligning with enterprise security guidelines.
Design, develop, and deploy production-grade traditional ML models including regression, classification, clustering, and recommender systems.
Design, maintain, and optimize end-to-end AI/ML pipelines across data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure such as AWS (or equivalent).
Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective in cloud environments.
Create reusable components, frameworks, and best practices to accelerate AI development.
Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval-Augmented Generation (RAG), and custom pipelines.
Design and develop interoperable AI agents using MCP and/or Google A2A.
Partner with data scientists, architects, product managers, business stakeholders, and technical teams to align AI solutions with organizational goals.
Provide hands-on technical support and mentorship to technical teams across the enterprise.

Requirements

3+ years of experience designing and deploying ML/AI solutions in real-world environments.
Very strong Python skills.
Strong hands-on experience with LLM APIs (including OpenAI, Azure OpenAI, Gemini, Anthropic) using Python and Python-based frameworks.
Strong hands-on experience with prompt engineering, context construction, and grounding strategies.
Strong hands-on experience with RAG workflows, including extracting, chunking, and creating embeddings from unstructured documents from sources such as O365 (email, Word, Excel), PDFs, and webpages.
Comfort building MCP clients, servers, and hosts.
Expertise building REST APIs and integrating with internal and external APIs.
Hands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents.
Knowledge of Google A2A.
Deep experience in AWS including Lambda, Bedrock, Step Functions, API Gateway, and IAM.
Strong experience with observability tools such as Dynatrace or similar GenAI observability tools.
Excellent GenAI foundations and concepts, including enterprise data privacy, AI governance, and observability.
Proficiency in Python and common ML/AI libraries such as TensorFlow, PyTorch, and scikit-learn.
Strong understanding of data engineering, SQL, and feature engineering.
Hands-on experience with cloud services including AWS SageMaker, Lambda, ECS, S3, and IAM.
Familiarity with containerization (Docker) and orchestration (e.g., Airflow, Kubeflow).
Working knowledge of version control and collaboration tools such as Git, Jira, and Confluence.
Bachelor’s degree in computer science, engineering, or a related field.
Knowledge of machine learning algorithms, deep learning frameworks, cloud AI technologies, GenAI technologies, and emerging agentic AI technologies.
Knowledge of cloud platforms (AWS, Azure, GCP) for scalable AI/ML development.
Knowledge of responsible AI principles including bias mitigation and ethical deployment.
Knowledge of ML Ops best practices including CI/CD for ML, model monitoring, and versioning.
Ability to build robust, scalable, and efficient AI/ML solutions in cloud-native environments.
Ability to translate ambiguous business problems into clear technical ML/AI tasks.
Ability to communicate complex ideas clearly to technical and non-technical stakeholders.
Ability to learn and adapt quickly to emerging AI technologies, techniques, and tools.

Technologies

AWS, SalesForce, Oracle, Snowflake, MS Copilot
AWS Textract, Bedrock, Model Context Protocol (MCP), Google A2A
OpenAI, Azure OpenAI, Gemini, Anthropic
REST APIs, Dynatrace
TensorFlow, PyTorch, scikit-learn
O365, PDFs, webpages
Intelligent Document Processing, OCR
AWS Lambda, AWS Step Functions, AWS API Gateway, AWS IAM, AWS Sagemaker, AWS ECS, AWS S3
Docker, Airflow, Kubeflow
Git, Jira, Confluence, CI/CD, ML Ops
Retrieval-Augmented Generation (RAG), prompt engineering, Context Engineering, grounding strategies, embeddings

Compensation & Logistics

Location: Minnesota (remote); local preferred but not required
Salary: USD 105,000 - 140,000 per year, plus 8% AIP eligibility
Start date: 9/28/2026

Benefits

Comprehensive Benefit Plan including medical, dental, vision, life insurance, short-term disability, long-term disability, paid sick leave, and retirement benefits
401(k) + Matching: match on the first 4% of contributions
Bonus opportunities including Longevity Award bonus and referral bonus
Professional development with on-demand training through a consultant portal, including upskilling in Artificial Intelligence (AI)
Ongoing support and networking through a Consultant Coach program

Preferred Qualifications

Master’s in a related technical field
Hands-on experience with agentic AI frameworks
Prior contributions to open-source AI/ML projects or published research
AI/ML certifications from cloud providers
Experience in highly regulated industries (e.g., healthcare, finance) a plus

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3mo ago

Lead Specialty Software Engineer - Capital Markets Reference Data

Wells Fargo Charlotte, NC

Charlotte, NC

3mo ago Closed

Full time Backend Developer Senior Ai Design Tools

This position is no longer accepting applications (closed Jul 3, 2026).

Job Description

The Lead Specialty Software Engineer will modernize and expand Wells Fargo's Reference Data Platform for Capital Markets, guiding the architecture, design, and development of high-performance data ingestion, mastering, and distribution of financial instruments reference data. The role emphasizes distributed systems, governance, risk management, and AI-enabled delivery, and is based onsite in Charlotte, North Carolina.

Responsibilities

Direct complex initiatives within designated domains, delivering scalable and resilient solutions.
Increase development velocity by applying agentic AI tools across engineering workflows.
Promote data quality and governance practices, including golden-source determination, cross-vendor reconciliation, and data lineage tracking across the reference data platform.
Ensure systems are monitored to improve operational efficiency and are managed to mitigate risk.
Identify opportunities to optimize resource utilization, streamline processes, and reduce costs.
Lead the end-to-end design, development, testing, and implementation of applications, system components, tools, utilities, models, simulations, and analytics for managing complex business functions using advanced technologies.
Troubleshoot coding, testing, and escalated platform issues of substantial complexity.
Guide the team to meet compliance and risk-management requirements, coordinating with stakeholders to implement key risk initiatives.
Mentor junior software engineers and foster professional growth.
Collaborate with professionals at all levels, including managers, to influence decisions and outcomes.

Requirements

5+ years of Specialty Software Engineering experience, or equivalent demonstrated through work experience, training, military service, or education.
5+ years designing and implementing distributed Java-based systems with expertise in memory management, concurrency, JMS, and native I/O.
3+ years using Java 17+ with modern features such as the Streams API, records, and enhanced concurrency.
3+ years building microservices, preferably within the Spring ecosystem (Boot, Batch, Data).
3+ years of SQL experience including complex query optimization.
2+ years of experience constructing event-driven architectures using Kafka, Solace, or similar technologies.

Technologies

Java, Java 17+, Streams API, records, enhanced concurrency
Spring (Boot, Batch, Data)
JMS, SQL, Kafka, Solace
Cloud and orchestration: Azure, GCP, OpenShift
CI/CD and automation: Harness, GitHub Actions
Testing: JUnit, Mockito
Artificial intelligence helpers: LLMs, prompt engineering, context engineering
Data and analytics: Python, pandas, PySpark
Frontend: React
In-memory data grids and caching: Apache Ignite, Redis, Coherence
API gateways

Job Expectations

Contribute as a hands-on software developer delivering components for the Reference Data platform.
Partner with Product Owners and business users to translate requirements into functional and technical specifications.
Analyze and refactor legacy systems to align with a scalable microservice architecture.
Document current-state and target-state architectures for platform modernization initiatives.
Produce high-quality, performant Java code applying object-oriented principles and established design patterns.
Perform thorough unit and functional testing and document development activities per team standards.
Ensure quality, maintainability, and extensibility of supported systems and risk applications.
Build resilient applications that require minimal production support.
Quickly ramp on new Capital Markets business domains within the technology stack.
Utilize agentic AI tools to accelerate delivery and sustain high code quality.
Identify opportunities to improve engineering processes and propose AI-driven solutions.

Posting Location

300 S Brevard St., Charlotte, NC 28202

Posting End Date

30 Jun 2026. Job posting may come down early due to volume of applicants.

Wells Fargo Recruitment and Hiring Requirements

Third-Party recordings are prohibited unless authorized by Wells Fargo.
Directly represent your own experiences during the recruiting and hiring process.

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AI Engineer

Lead Specialty Software Engineer - Capital Markets Reference Data

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