AI/ML Engineer

Be The Match in

Minneapolis (MN)

On-site

USD 120,000 - 180,000

Full time

8 days ago

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Job summary

Be The Match seeks an AI/ML Engineer to design, develop, and deploy GenAI, AI, and traditional ML solutions across the organization. You will work across platforms like AWS, Salesforce, and Snowflake to automate workflows and improve user experiences.

You will build scalable ML models, implement end-to-end AI/ML pipelines in cloud environments, and create reusable components and frameworks to accelerate development. Collaboration with cross‑functional teams is essential.

Qualifications

Bachelor's degree in computer science, engineering, or a related field.
3+ years designing and deploying ML/AI solutions in real-world environments.
Strong Python skills and hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic).
Experience with prompt engineering, context construction, grounding strategies.
Experience with Retrieval Augmented Generation (RAG) and embedding creation from diverse sources.
Knowledge of MCP clients/servers and building REST APIs.
Hands-on with cloud services (AWS Sagemaker, Lambda, ECS, S3, IAM) and Docker.

Responsibilities

Work across GenAI platforms and libraries to design and implement AI/ML solutions.
Develop and maintain end-to-end AI/ML pipelines on cloud infrastructure.
Create reusable components and best practices to accelerate AI development.
Design GenAI solutions using prompt engineering, Context Engineering, and RAG.
Collaborate with stakeholders to translate business problems into ML tasks.
Ensure security, reliability, and observability of AI systems.

Skills

Python programming

LLM APIs

Prompt engineering

Context engineering

RAG

MCP

REST APIs

Cloud familiarity

Observability

Git/Jira/Confluence

Education

Bachelor's degree in CS/Engineering

Master's preferred

Tools

AWS

Bedrock

Azure/AWS/GCP stack

Docker

Sagemaker

Lambda

API Gateway

Job description

The AI/ML Engineer will play a crucial role in the AI Center of Excellence (CoE), supporting cross‑functional teams across the organization. This position will focus on designing, developing and developing machine learning (ML), artificial intelligence (AI), Generative AI (GenAI) and Agentic AI solutions to address domain specific needs, improve user experiences and automate business workflows.

ACCOUNTABILITIES

Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries.
Automate workflows involving extraction of complex, multimodal unstructured content from variety of sources in to highly accurate and reliable structured content using platforms like AWS Textract and Bedrock
Design and build MCP hosts, clients and servers
Establish and use frameworks for automated LLM testing
Create regression test suites to detect drift or prompt breakage
Integrate with internal and external web services using secure authentication and authorization mechanisms
Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines
Design, develop, and deploy production‑grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases.
Design, maintain, and optimize end‑to‑end AI/ML pipelines including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (e.g., AWS or equivalent)
Ensure AI/ML solutions are scalable, reliable, secure, and cost‑effective within cloud environments
Create reusable components, frameworks, and best practices to accelerate AI development

AI/ML Engineer (Finance)

Job Description

ACCOUNTABILITIES

Engineering

Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries.
Automate workflows involving extraction of complex, multimodal unstructured content from variety of sources in to highly accurate and reliable structured content using platforms like AWS Textract and Bedrock
Design and build MCP hosts, clients and servers
Establish and use frameworks for automated LLM testing
Create regression test suites to detect drift or prompt breakage
Integrate with internal and external web services using secure authentication and authorization mechanisms
Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines
Design, develop, and deploy production‑grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases.
Design, maintain, and optimize end‑to‑end AI/ML pipelines including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (e.g., AWS or equivalent)
Ensure AI/ML solutions are scalable, reliable, secure, and cost‑effective within cloud environments
Create reusable components, frameworks, and best practices to accelerate AI development

Design and Innovation

Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval‑Augmented Generation (RAG), and custom pipelines
Design and develop interoperable AI agents using Model Context Protocol (MCP) and/or Google A2A

Collaboration and Enablement

Partner with data scientists, architects, product managers, business stakeholders and technical teams across organization to align AI solutions with organizational goals.
Provide hands‑on technical support and mentorship to technical teams across the enterprise.

REQUIRED QUALIFICATIONS

Knowledge of

Machine learning algorithms, deep learning frameworks, Cloud AI technologies, GenAI technologies and emerging Agentic AI technologies.
Cloud platforms (e.g., AWS, Azure, GCP) for scalable AI/ML development.
Responsible AI principles, including bias mitigation and ethical deployment.
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.
Translate ambiguous business problems into clear, technical ML/AI tasks.
Communicate complex ideas clearly to technical and non‑technical stakeholders.
Learn and adapt quickly to emerging AI technologies, techniques, and tools.

Education and/or Experience

Bachelor's degree in computer science, Engineering, or related field.
3+ years of experience designing and deploying ML/AI solutions in real‑world environments in which time candidates will have created strong proficiency with the following:
- Very strong Python skills with strong hands‑on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.) using Python and Python based frameworks
- Strong hands‑on experience in prompt engineering, context construction, grounding strategies
- Strong hands‑on experience with Retrieval Augmented Generation (RAG) extracting, chunking and create embeddings from unstructured documents from diverse sources including O365(email, word, excel), PDFs, and webpages.
- Comfortable building Model Context Protocol (MCP) clients, servers and hosts.
- Strong Expertise in building REST APIs and integrating with internal/external APIs
- Hands‑on experience with Intelligent Document Processing and/or OCR technologies on complex documents
- Knowledge of Google A2A
- Deep experience in AWS (Lambda, Bedrock, Step Functions, API Gateway, IAM)
- Strong experience with Observability tools like Dynatrace, or other similar GenAI observability tools
- Excellent GenAI foundations and concepts
- Clear understanding of enterprise data privacy, AI governance, and observability
- Proficiency in Python and common ML/AI libraries (e.g., TensorFlow, PyTorch, scikit‑learn).
- Strong understanding of data engineering, SQL, and feature engineering.
- Hands‑on experience with cloud services such as AWS Sagemaker, Lambda, ECS, S3, and IAM.
- Familiarity with containerization (Docker) and orchestration (e.g., Airflow, Kubeflow).
- Working with version control and collaboration tools (Git, Jira, Confluence etc).