Senior AI Engineer

Role Overview

We are seeking a hands-on Senior AI Engineer with a strong foundation in traditional Machine Learning and practical, real-world experience building and deploying LLM- and GenAI-driven systems. This role focuses on designing, engineering, and hardening production-grade AI solutions that are embedded into business workflows—not research prototypes.

You will work in small, high-impact delivery teams (2–3 engineers per initiative) and spend the majority of your time (~70–75%) building systems end to end, while also contributing to solution design, technical decision-making, and cross-functional collaboration.

Required Skills & Experience

Software & Systems Engineering

• 10-12 years of overall software engineering experience, including prior work as an ML Engineer or equivalent.
• Strong backend development skills (Python, Java, Node.js, or similar languages).
• Experience designing and building REST or gRPC-based services.
• Solid understanding of distributed system design.
• Containerization and orchestration experience (Docker, Kubernetes).

AI / ML

• Hands-on experience across traditional ML and modern GenAI systems.
• Proficiency with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or equivalents.
• Experience building or deploying: 
  • ML-driven production systems
  • LLM-based applications
• Ability to select ML vs. LLM-driven approaches based on business and operational constraints.

Cloud & DevOps

• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
• Experience with CI/CD pipelines and deployment automation.
• Understanding of model, code, and configuration versioning best practices.

Observability & Production Readiness

• Experience implementing logging, monitoring, and tracing for production systems.
• Familiarity with system resilience patterns such as: 
  • Rate limiting
  • Failover strategies
  • Kill-switch mechanisms

Problem Solving & Mindset

• Strong ability to solve ambiguous, real-world engineering problems.
• Comfortable working in fast-moving, iterative environments.
• Ownership mindset with a bias toward practical, scalable solutions.

Communication & Collaboration

• Experience working in cross-functional teams.
• Ability to clearly articulate technical and business trade-offs, including: 
  • LLM vs traditional ML
  • Build vs buy decisions
  • Speed vs robustness

Good to Have

• Experience with enterprise AI platforms or internal AI frameworks.
• Prior production experience with: 
  • Agentic architectures
  • Multi-agent systems
  • RAG-based systems at scale
• Exposure to AI governance, safety, and compliance considerations.
• Experience mentoring junior engineers or owning technical modules.
• Hands-on experience optimizing performance and cost for AI workloads.