Data Engineer

We are looking for a AI Engineer to lead the design, development, and deployment of AI-powered systems across the organization. This role sits at the intersection of full-stack software engineering and applied AI, with a strong emphasis on large language models (LLMs), generative AI, and production-grade ML infrastructure. You will push beyond conventional approaches to explore the advanced possibilities of AI, including autonomous agents, multi-model orchestration, and novel architectures.

This is a hands-on engineering role for someone who can build reliable, scalable AI systems from prototyping and prompt engineering through to production deployment, monitoring, and iteration.

• Design, build, and maintain production AI/ML systems, including LLM-based applications, agentic workflows, multi-model pipelines, and advanced retrieval systems
• Explore and prototype cutting-edge AI capabilities beyond standard patterns, such as autonomous agents, tool-use frameworks, reasoning chains, and self-improving systems
• Develop and optimize prompt engineering strategies, fine-tuning approaches, and evaluation frameworks for generative AI models
• Architect and implement MLOps pipelines for model training, versioning, deployment, and monitoring using Azure DevOps and cloud infrastructure
• Build robust APIs, backend services, and frontend interfaces to integrate AI capabilities into internal and customer-facing products
• Evaluate and integrate foundation models (OpenAI, Anthropic, Google) based on performance, cost, and latency requirements
• Implement guardrails, safety mechanisms, and observability tooling for deployed AI systems
• Collaborate with product, data, and engineering teams to identify high-impact AI use cases and translate them into technical solutions
• Follow engineering best practices, including code review, testing, CI/CD, and documentation for AI codebases
• Stay current with the rapidly evolving AI landscape and drive adoption of emerging tools, frameworks, and techniques, * AI systems are deployed to production with clear SLAs, monitoring, and rollback capabilities
• LLM-powered features deliver measurable business value with consistent quality and reliability
• The team is actively exploring and shipping advanced AI capabilities that go beyond industry-standard approaches
• MLOps infrastructure enables rapid experimentation and safe, repeatable model deployments
• AI is treated as a core engineering discipline with proper testing, documentation, and operational rigor

• Strong software engineering fundamentals in Python and JavaScript, with experience in production-grade application development
• Proficiency in modern JavaScript frameworks such as React and Angular for building AI-powered user interfaces and tooling
• Deep hands-on experience with LLMs, prompt engineering, and generative AI frameworks (LangChain, LlamaIndex, or similar)
• Experience with advanced AI patterns beyond basic RAG, including autonomous agents, multi-step reasoning, tool use, and multi-model orchestration
• Proficiency in MLOps tooling and practices: model serving (e.g., vLLM, TGI), experiment tracking, and CI/CD for ML
• Strong experience with Azure DevOps for pipeline management, release automation, and infrastructure-as-code
• Solid knowledge of cloud infrastructure, networking, and security on AWS and Azure
• Hands-on experience with AWS services including S3, Lambda, EC2, and related compute and storage offerings
• Working knowledge of relational and specialized databases, including PostgreSQL, Amazon Redshift, Qdrant (vector database), and Redis
• Experience with containerized deployments (Docker, Kubernetes) and infrastructure automation
• Solid understanding of software architecture patterns, API design (REST/GraphQL), and distributed systems
• Experience with model evaluation, A/B testing, and performance benchmarking for AI systems, * You build AI systems that are production-ready from day one: reliable, observable, and maintainable
• You think beyond off-the-shelf solutions and are excited to explore what is newly possible with AI
• You think in terms of architecture and scalability, not just proof-of-concept demos
• You proactively identify failure modes, edge cases, and safety considerations before they reach production
• You can operate independently while collaborating effectively across engineering, product, and data teams
• You communicate complex technical concepts clearly to both technical and non-technical stakeholders