Generative AI/ML Engineer — Onsite

Slalom; Washington (District of Columbia); On-site; USD 106,000 - 166,000; Full time

Benefits offered by this job

401(k) with match

Health, dental, & vision coverage

Well-being reimbursement

Job summary

Slalom is seeking a technologist to design, build, and deploy generative AI and ML solutions for client problems in a fast-paced environment. You will develop LLM-powered applications, employ RAG, agents, prompt engineering, and TF tooling while applying ML/DL techniques to real business needs.

The role emphasizes production-ready ML, strong Python skills, and collaboration across data science and data engineering teams, with opportunities to mentor others and advance the AI practice.

Qualifications

• 3+ years of experience implementing models or machine learning algorithms into production.
• 2-4 years of professional Managed or IT Professional consulting experience.
• Excellent python, especially for ML/AI use cases.

Responsibilities

• Design, build, and deploy generative AI and machine learning solutions that solve business problems and improve how people work.
• Develop LLM-powered applications using RAG, agents, prompt engineering, tool and function calling, workflow automation, and fine-tuning where appropriate.
• Apply Machine Learning, Deep Learning, and other relevant technologies to assist in building AI tools that solve real business problems.
• Assist in the design, development, presentation, and delivery of traditional ML solutions in areas such as Computer Vision, NLP, Recommendation Systems, etc.

Skills

ML model deployment

Consulting experience

Python (ML/AI)

Communication skills

Education

Data Science / Data Engineering background

Tools

Databricks

Snowflake

MLflow

Job description

Slalom is seeking a technologist to design, build, and deploy generative AI and ML solutions for client problems in a fast-paced environment. You will develop LLM-powered applications, employ RAG, agents, prompt engineering, and TF tooling while applying ML/DL techniques to real business needs.

The role emphasizes production-ready ML, strong Python skills, and collaboration across data science and data engineering teams, with opportunities to mentor others and advance the AI practice.