AI/ML Engineer at Latcha + Associates

Farmington Hills, Michigan, United States -

Full Time

Start Date

Immediate

Expiry Date

07 Jan, 26

Salary

0.0

Posted On

09 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, AI Engineering, LLM Engineering, Data Integration, MLOps, Deployment, Collaboration, Research, Innovation, SQL, Data Engineering, Cloud Environment, Azure DevOps, GitHub Actions, Marketing Tech, Ad Tech

Industry

Advertising Services

Description

Latcha+Associates is a marketing firm located in southeastern Michigan. We specialize in supporting our automotive clients with innovative marketing solutions. We are seeking an AI/ML Engineer to join our growing data and AI team. This individual will be responsible for developing, deploying, and scaling AI-powered solutions that directly impact marketing performance, client personalization, and internal automation. You will work closely with our data engineering team to leverage our existing data platform, and with business stakeholders to build AI agents and applications that deliver measurable business value. This role is both hands-on and strategic — ideal for someone who can rapidly prototype with LLMs and machine learning models, while also designing robust pipelines for training, retraining, and production deployment. Responsibilities AI Agent Development: Build and deploy AI agents that automate workflows, content creation, campaign optimization, and internal decision support. LLM Engineering: Train, fine-tune, and retrain large language models (LLMs) on proprietary data; manage model evaluation and continuous improvement. Data Integration: Work with data engineers to access and preprocess structured and unstructured data from Delta tables, APIs, and external sources. MLOps & Deployment: Implement scalable pipelines for model training, deployment, monitoring, and retraining using modern MLOps practices. Prototype → Production: Rapidly build proofs-of-concept, then transition validated models into production systems. Collaboration: Partner with analysts, engineers, and business stakeholders to identify use cases, define success metrics, and deliver end-to-end AI solutions. Research & Innovation: Stay current with emerging AI/ML techniques, frameworks, and tools. Required Qualifications: 3–6 years of experience in applied machine learning or AI engineering. Proficiency in Python with strong experience in ML/AI frameworks (PyTorch, TensorFlow, Hugging Face). Experience working with LLMs (training, fine-tuning, prompt engineering, RAG). Familiarity with MLOps tools (MLflow, Databricks, etc.). Strong SQL and data engineering fundamentals; ability to work with large datasets. Hands-on experience deploying AI solutions into production in a cloud environment experience with Azure DevOps/GitHub Actions a plus Preferred/Nice-to-Have: Experience in marketing tech, ad tech, or personalization systems – automotive a big plus. Familiarity with LangChain, LlamaIndex, RAG pipelines, and vector databases (Pinecone, Weaviate, FAISS, etc.). Deep working knowledge of cloud platforms, ideally Azure Databricks. Strong understanding of model governance, bias, and ethical AI considerations. What Success In This Role Looks Like: Within 3 months: You’ve delivered a working AI prototype (e.g., an agent that generates campaign briefs or automates audience creation). Within 6 months: You’ve deployed at least one AI/ML system into production, with monitoring and retraining in place. Within 12 months: You’re leading the design of scalable AI capabilities, driving measurable revenue or efficiency gains for the business. For certain job opportunities, you may be required to take a technical assessment, which is a standardized test that evaluates job-relevant skills and capabilities. Technical assessments may take the form of online tests or technical interviews where you demonstrate your skills live with an interviewer.

Responsibilities

The AI/ML Engineer will develop, deploy, and scale AI-powered solutions that enhance marketing performance and client personalization. This role involves building AI agents, integrating data, and implementing MLOps practices for production deployment.