- Company Name
- Leute Passen Technologies
- Job Title
- Senior AIML Engineer
- Job Description
-
**Job title**
Senior AI/ML Engineer (LLM & Client‑Facing)
**Role Summary**
Architect, implement, and deliver large‑language‑model (LLM) solutions end‑to‑end while acting as the primary technical liaison for enterprise clients and leading a high‑performance AI delivery team.
**Expactations**
- Deliver multiple production‑ready LLM projects within the first year, meeting defined success metrics.
- Become the trusted AI advisor for key clients and internal stakeholders.
- Design reusable frameworks, patterns, and governance practices that elevate team productivity.
- Assume clear leadership responsibilities, mentoring and expanding the AI team.
**Key Responsibilities**
- Design and architect LLM systems (RAG, fine‑tuning, agents, copilots) from concept through production.
- Build end‑to‑end AI pipelines: data ingestion, model evaluation, deployment, monitoring, and optimization.
- Evaluate, select, and tune commercial and open‑source LLMs (GPT, Claude, LLaMA, etc.) for performance, cost, and security.
- Design scalable, secure, cloud‑based AI architectures on AWS, Azure, or GCP.
- Serve as the main technical point of contact for enterprise clients, translating business needs into technical solutions.
- Lead workshops, solution walkthroughs, and presentations for technical and executive audiences.
- Own project delivery schedules, risk management, and scope control.
- Mentor junior and mid‑level data scientists and ML engineers; contribute to hiring, onboarding, and setting technical standards.
- Implement robust code quality, documentation, model governance, and MLOps practices (CI/CD, containerization, monitoring).
**Required Skills**
- 6+ years in data science, machine learning, or AI engineering.
- Hands‑on experience building and deploying LLM applications (RAG, agents, fine‑tuning) in production.
- Proficiency in Python and deep‑learning frameworks (PyTorch or TensorFlow).
- Deep expertise in prompt engineering, RAG architectures, fine‑tuning, and inference optimization.
- Strong cloud deployment and monitoring skills on AWS, Azure, or GCP.
- Proven client‑facing, consulting, or stakeholder management with excellent communication and presentation skills.
- MLOps experience (CI/CD, containerization, model monitoring, MLflow, Weights & Biases).
- Familiarity with AI governance, responsible AI, data privacy, and security standards.
- Experience in regulated industries (finance, healthcare, enterprise SaaS) is a plus.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
- Relevant certifications such as AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate, or similar are highly desirable.