Design and deliver advanced AI solutions
Lead the architecture and delivery of end‑to‑end AI systems, including ML models, LLM-based components, APIs, and scalable cloud deployments
Drive technical decision‑making and act as the primary engineering advisor for the team
Guide the AI engineering team as they improve their DevOps skills and ensure they consistently follow best practices throughout the SDLC process
Implement high‑quality, highly scalable, maintainable, production-grade code using modern engineering practices
Integrate AI components with enterprise applications, data sources, and third‑party systems
Ensure adherence to DevOps/MLOps best practices, including CI/CD, observability, testing, and cloud‑native workflows
Stay ahead of market trends and emerging technologies in ML, GenAI, LLMs, MLOps, Agentic Workflows, software engineering, and cloud platforms
Introduce new tools, frameworks, and patterns that elevate our AI delivery capability
Guide internal teams and clients on AI adoption, feasibility, risks, and implementation strategy
Support business development efforts by contributing to proposals, solution blueprints, and technical presentations
Identify new opportunities where advanced analytics and AI can unlock value for our clients
Mentor junior engineers and contribute to the broader PwC engineering community
Lead knowledge-sharing initiatives and support capability building across the firm

Degree in Computer Science, Electrical & Computer Engineering, or a related field; PhD is a plus
Six (6) + years of professional experience in production-based software engineering, ML engineering, AI systems, AI engineering, or data‑intensive application development
Demonstrated technical leadership or ownership of complex systems
Strong programming skills in Python (preferred), .NET, Java, or similar languages
Hands‑on experience with ML frameworks (e.g., PyTorch, TensorFlow, Scikit‑learn) and modern GenAI tooling (Langchain, Langraph or similar orchestration frameworks)
Solid understanding of databases, SQL, and data engineering concepts; experience with large-scale datasets or Apache Spark is a plus
Experience with cloud platforms (Azure preferred; AWS or GCP also valuable)
Ability to design scalable, secure, and resilient systems following DevOps and cloud-native practices
Excellent communication skills with the ability to explain complex concepts to non-technical stakeholders
High adaptability to evolving requirements and dynamic project environments
Strong curiosity and passion for continuous learning in the rapidly evolving AI landscape

Your work life-balance supported by a hybrid working model
Your creativity sparked in collaborative office spaces
Your career growing through local and global opportunities
Your development advanced with continuous learning and professional certifications
Your well-being cared for with extra leave days and wellness initiatives
Your perspective valued in an inclusive team where your impact matters

Principal Engineer – Analytics, AI

Key skills