The AI/ML Engineer designs, develops, and deploys Generative AI and traditional machine learning solutions across the BEUSA family of companies to drive measurable impact.
Requirements
- Proficiency in Python and common ML/AI libraries and tools (e.g., scikit-learn, PyTorch or TensorFlow, Transformers, LangChain/LlamaIndex or equivalent).
- Practical experience with LLMs and Generative AI (prompt engineering, RAG, embeddings, vector databases, safety/guardrails, evaluation).
- Working knowledge of MLOps best practices: experimentation, versioning, CI/CD, containerization, monitoring, and observability.
- Experience deploying in cloud environments (AWS, Azure, or GCP) and using services relevant to data/ML (e.g., serverless, Kubernetes, managed ML services).
- Ability to design and optimize data pipelines (batch/stream) and model serving workflows.
- 2–5 years of professional experience developing and deploying machine learning models in production.
- 1+ year of hands-on experience implementing Generative AI solutions in production or pilot environments.
Responsibilities
- Design, implement, and deploy scalable AI/ML models (with emphasis on Generative AI applications such as LLMs, retrieval-augmented generation, and prompt engineering).
- Build robust data pipelines, feature engineering workflows, and training/evaluation jobs using Python and standard ML libraries.
- Package and deploy models as services or batch jobs; implement inference pipelines and optimize for latency, throughput, and cost.
- Evaluate and integrate Generative AI models and frameworks (e.g., LLMs, embeddings, vector search, diffusion models) for defined use cases.
- Develop prompts, RAG pipelines, guardrails, and evaluation harnesses; conduct A/B and offline evaluations to improve output quality and safety.
- Apply best practices for experiment tracking, model versioning, CI/CD, monitoring, and alerting.
- Implement data and model quality checks, drift detection, and performance dashboards.
Other
- Successfully passes background check, pre-employment drug screening, and any pre-employment aptitude and/or competency assessment(s).
- Proficiency in spoken English language.
- Daily in-person, predictable attendance.
- Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Mathematics, or a related field.
- Excellent verbal and written communication skills, with the ability to present technical topics to both technical and non-technical audiences.
