Job Description

AI/ML Engineer II

We are seeking a highly capable AI/ML Engineer II to serve as a strong individual contributor within our Artificial Intelligence and Automation team. The ideal candidate has a track record of delivering complex, production-grade Machine Learning and Generative AI systems and can own significant modules end to end with minimal supervision. You will lead the technical design of AI features across LLMs, Agentic AI, Retrieval-Augmented Generation (RAG), MLOps, and Intelligent Automation, and set a high bar for engineering quality.

You will act as a go-to technical resource on your projects, mentor less-experienced engineers, and collaborate with architects and stakeholders to deliver scalable, reliable AI solutions.

KEY RESPONSIBILITIES

• Lead the technical design and delivery of complex ML and Generative AI components, owning them from requirements through production.
• Develop, fine-tune, and optimize models and LLM pipelines with strong attention to accuracy, latency, cost, and reliability.
• Architect and improve advanced RAG systems, including hybrid retrieval, re-ranking, chunking strategy, and evaluation.
• Design and build production Agentic AI and multi-agent workflows with robust tool use, orchestration, and error handling.
• Drive engineering quality: establish patterns, conduct thorough code and design reviews, and reduce technical debt.
• Build and harden MLOps/LLMOps pipelines — CI/CD, automated evaluation, monitoring, drift detection, and retraining.
• Design integrations with enterprise applications, APIs, data platforms, and vector stores at scale.
• Define and apply prompt-engineering standards, evaluation frameworks, and guardrails for safe, high-quality LLM systems.
• Partner with product and business stakeholders to shape solution scope, estimate effort, and manage delivery risk.
• Mentor junior and mid-level engineers, and contribute to reusable frameworks, libraries, and accelerators.
• Evaluate emerging models, tools, and techniques and pilot them for practical enterprise use.
• Produce high-quality technical documentation, design specifications, and deployment artifacts.

REQUIRED QUALIFICATIONS

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3-7 years of hands-on experience in AI, Machine Learning, or related software engineering roles, including delivering systems to production.
• Advanced Python engineering skills and strong software-design fundamentals.
• Deep, practical experience building and optimizing Generative AI and LLM-based applications.
• Strong experience with agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen) and production RAG architectures.
• Solid MLOps experience: CI/CD, model deployment, monitoring, and evaluation.
• Proven experience with at least one major cloud platform (Azure, AWS, or Google Cloud), Docker, and containerized deployments.
• Demonstrated ability to own complex modules independently and lead technical decisions on a project.
• Experience mentoring engineers and driving engineering best practices.
• Excellent communication and cross-functional collaboration skills.

PREFERRED SKILLS

• Strong proficiency across PyTorch, TensorFlow, Hugging Face, and the broader Python data/ML ecosystem.
• Production experience with multiple LLM providers (OpenAI, Anthropic Claude, Azure OpenAI, Gemini) and open-weight models.
• Experience with fine-tuning, LoRA/PEFT, and model optimization or serving (vLLM, TGI, Triton).
• Advanced experience with vector databases and large-scale retrieval.
• Experience with orchestration and workflow tooling such as Kubernetes, Airflow, or Ray.
• Experience building LLM evaluation, observability, and guardrail frameworks.
• Familiarity with responsible-AI, security, and governance practices.
• Open-source contributions, publications, or a strong portfolio of applied AI work.

WHAT YOU'LL GAIN

• Technical ownership of complex, high-visibility AI and Automation systems.
• A path toward senior and principal technical-leadership roles.
• Deep involvement in Agentic AI, LLM, and next-generation AI platform work.
• Influence over engineering standards, patterns, and reusable accelerators.
• The opportunity to mentor engineers and shape team technical capability.