About The Role

JOB SUMMARY

We are seeking a Senior AI/ML Engineer to provide technical leadership within our Artificial Intelligence and Automation team. The ideal candidate combines deep, hands-on expertise in building and scaling Machine Learning and Generative AI systems with the ability to lead the design and delivery of major initiatives. You will drive the technical direction of significant AI programs across LLMs, Agentic AI, Retrieval-Augmented Generation (RAG), MLOps platforms, and Intelligent Automation.

As a senior technical leader, you will own the design of complex systems, raise engineering standards, mentor engineers across levels, and partner with stakeholders to align solutions with business goals.

KEY RESPONSIBILITIES

Lead the technical design and delivery of large, complex ML and LLM systems, balancing performance, cost, reliability, and safety.
Own the architecture of major AI features and services, making key design and technology decisions.
Design and scale advanced RAG and knowledge systems, including hybrid retrieval, re-ranking, evaluation, and continuous improvement.
Lead the design of sophisticated Agentic AI and multi-agent systems, including orchestration, tool use, and reliability.
Mature the team's MLOps/LLMOps practices: CI/CD, model registries, monitoring, evaluation, drift detection, and automated retraining.
Establish engineering standards, patterns, and reusable frameworks; drive code and architecture reviews.
Apply and promote responsible-AI, security, guardrail, and cost-optimization practices across projects.
Lead cross-functional delivery of complex programs, managing technical risk and dependencies.
Mentor and coach engineers across experience levels and grow overall team capability.
Partner with product and business leaders to shape solution direction, prioritize work, and quantify business impact.
Evaluate and pilot emerging models, tools, and techniques, translating them into pragmatic solutions.
Produce and review high-quality design documentation, technical specifications, and deployment artifacts.

REQUIRED QUALIFICATIONS

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (advanced degree preferred).
8-15 years of professional experience in AI, Machine Learning, or software engineering, including substantial time delivering ML/AI systems to production.
Expert-level Python engineering skills and a strong software-architecture background.
Proven track record designing and scaling Generative AI and LLM-based systems in production.
Deep, hands-on experience with agentic AI frameworks and complex RAG architectures.
Strong MLOps/LLMOps expertise: CI/CD, monitoring, evaluation, and lifecycle automation.
Extensive experience with at least one major cloud platform (Azure, AWS, or Google Cloud) and containerized, distributed systems.
Demonstrated technical leadership: leading designs, mentoring engineers, and setting standards.
Solid understanding of responsible AI, security, governance, and cost optimization.
Excellent communication skills, able to influence both engineers and senior stakeholders.

PREFERRED SKILLS

Deep proficiency across the modern AI stack: PyTorch, TensorFlow, Hugging Face, and distributed training/inference.
Production experience with multiple LLM providers (OpenAI, Anthropic Claude, Azure OpenAI, Gemini) and open-weight models.
Experience with fine-tuning, LoRA/PEFT, distillation, and model optimization/serving (vLLM, TGI, Triton).
Advanced experience with vector databases and large-scale retrieval systems.
Expertise with orchestration and platform tooling such as Kubernetes, Airflow, and Ray.
Experience building LLM evaluation, observability, and guardrail frameworks.
Track record of introducing reusable platforms, frameworks, or accelerators.
Open-source contributions, publications, patents, or conference talks in the AI community.

WHAT THIS ROLE OFFERS

Technical leadership of high-impact, enterprise-scale AI and Automation programs.
Deep involvement in architecting next-generation Agentic AI and LLM systems.
Influence over engineering standards, responsible-AI practices, and team capability.
A clear path toward Principal-level technical leadership.
The opportunity to mentor and grow a strong AI engineering team.