Principal Engineer

About the Role

We are looking for a highly experienced Principal AI Engineer to lead the design, development, and scaling of our AI Platform and Generative AI capabilities. The ideal candidate will have deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, LLMOps, and distributed systems.

This role requires a strong combination of hands-on technical expertise, system design capabilities, and technical leadership to build scalable, reliable, secure, and production-ready AI solutions. The candidate will drive AI architecture and engineering best practices, mentor engineering teams, and collaborate with cross-functional stakeholders to translate business requirements into impactful AI-powered products and platforms.

Key Responsibilities

• AI Platform Engineering: Architect and build scalable AI platforms that support enterprise-wide Generative AI applications, model serving, orchestration, and AI-powered workflows.

• Generative AI & LLMs: Design and implement production-grade solutions using LLMs, prompt engineering, model integration, fine-tuning, inference optimization, and evaluation frameworks.

• RAG & Knowledge Systems: Develop advanced RAG pipelines involving document ingestion, chunking, embedding generation, vector databases, retrieval optimization, reranking, and context management.

• Agentic AI: Build intelligent AI agents and multi-agent workflows with tool calling, planning, memory, orchestration, and reliable execution of complex tasks.

• LLMOps & MLOps: Establish end-to-end practices for deploying, monitoring, evaluating, versioning, and continuously improving AI/ML models and LLM-powered applications.

• Distributed Systems & Microservices: Design resilient, high-throughput, low-latency distributed systems and microservices to support AI workloads at scale.

• Cloud-Native Infrastructure: Leverage Kubernetes, containerization, CI/CD, and cloud-native technologies to deploy and scale AI services reliably.

• System Architecture & Design: Drive architecture decisions, design reviews, technology selection, and engineering standards for complex AI systems.

• Technical Leadership: Provide technical direction to engineering teams, mentor senior engineers, conduct code and architecture reviews, and establish engineering best practices.

• Engineering Excellence: Drive improvements in system reliability, scalability, observability, security, performance, and cost efficiency across AI infrastructure and applications.

• Cross-Functional Collaboration: Partner with Product, Data Science, ML Engineering, and business teams to identify AI opportunities and deliver measurable business outcomes.

Required Skills & Experience

• Strong experience in AI Platform Engineering, Generative AI, and LLM-based application development.

• Deep understanding of LLM architectures, inference, prompt engineering, model evaluation, and optimization techniques.

• Hands-on expertise in RAG, vector databases, embeddings, semantic search, and retrieval optimization.

• Experience designing and deploying Agentic AI systems, including tool calling, workflow orchestration, and multi-agent architectures.

• Strong expertise in LLMOps and MLOps, including model deployment, monitoring, evaluation, observability, and lifecycle management.

• Strong programming skills in Python, with experience building production-grade backend services and AI applications.

• Extensive experience with distributed systems, microservices architecture, APIs, and event-driven systems.

• Hands-on experience with Kubernetes, Docker, CI/CD pipelines, and cloud-native deployment practices.

• Strong system design skills, with the ability to architect highly scalable, fault-tolerant, secure, and performant systems.

• Demonstrated experience in technical leadership, architecture ownership, mentoring engineers, and driving engineering initiatives.

• Ability to balance technical depth, architectural vision, delivery timelines, and business priorities.

Preferred Qualifications

• Experience building AI platforms used by multiple engineering or business teams.

• Experience with LLM orchestration frameworks, model gateways, inference serving, and evaluation pipelines.

• Familiarity with observability tools, distributed tracing, performance profiling, and AI-specific monitoring.

• Experience optimizing LLM latency, throughput, token consumption, infrastructure utilization, and overall inference costs.

• Experience implementing guardrails, AI safety controls, access management, and governance for production AI systems.

• Experience building AI-powered products and platforms that operate at significant scale.

What We Are Looking For

• A hands-on technical leader who can drive architecture while remaining deeply involved in critical engineering decisions.

• An engineer who can take AI solutions from experimentation and proof of concept to reliable production deployment.

• Strong problem-solving and analytical skills, with the ability to simplify complex technical challenges.

• Excellent communication and stakeholder management skills, with the ability to influence technical direction across teams.

• A strong ownership mindset and a track record of delivering scalable, high-quality engineering solutions.