AI Applied Senior Engineering Lead

Solid understanding of core AI concepts such as knowledge representation, planning, and multi‑agent systems.

Hands‑on experience with LLMs, RAG, prompt engineering, MCPs, and agent frameworks (e.g., Google ADK).

Practical experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit‑learn, NumPy, Pandas).

Familiarity with AI coding tools such as Devin, Claude Code, GitHub Copilot, and Antigravity.

Software Engineering: Strong grounding in modern engineering practices including Git, CI/CD, testing, code reviews, agile delivery, application resiliency, and security.

Architecture: Experience designing API‑first, microservices‑based and event‑driven architectures, including data engineering patterns for AI systems.

Platforms: Hands‑on experience with Docker, Kubernetes, and OpenShift.

Problem Solving & Communication: Strong analytical skills with the ability to clearly communicate complex technical concepts.

Domain Knowledge: Solid understanding of banking/financial services and regulated environments.

Nice to Have: Experience in data science (statistical modeling, experimentation) and interest in mentoring junior AI engineers.

10+ years of progressive software engineering experience with a strong hands‑on coding background.

2+ years focused on AI software development, LLM‑based solutions, agentic systems, and/or machine learning.

Demonstrated experience delivering enterprise‑scale AI capabilities into production.

Education: Bachelor’s or Master’s degree in Computer Science, AI, Robotics, or a related quantitative discipline.

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.

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