Senior AI Software Engineer

Responsibilities

• Lead the design and development of production-grade LLM applications and AI-powered enterprise system
• Architect scalable AI system components, including data pipelines, model integration, orchestration layers, evaluation workflows, and deployment infrastructure
• Collaborate with product, software, and business teams to translate enterprise needs into reliable AI solutions
• Design and orchestrate LLM-based workflows using modern frameworks, tools, and cloud-native architectures
• Drive proof-of-concept initiatives and turn promising ideas into scalable production solutions
• Adapt, fine-tune, and optimize machine learning and generative AI models where needed
• Implement and improve MLOps practices using containerization, Kubernetes, MLflow, cloud services, and CI/CD pipelines
• Evaluate AI applications in terms of quality, reliability, latency, cost, safety, and business impact
• Mentor engineers on AI engineering best practices, code quality, and production readiness
• Follow emerging AI techniques and share insights through prototypes, technical documentation, and internal knowledge-sharing
• Advocate for responsible AI principles, ensuring fairness, transparency, privacy, and security

Qualifications

• BSc, MSc, or PhD in Computer Science, Engineering, or a related field
• Strong hands-on experience with Python and modern machine learning frameworks such as PyTorch or TensorFlow
• Proven experience designing, building, and deploying production-grade AI, ML, or LLM-based systems
• Solid understanding of transformer-based architectures and generative AI systems
• Experience adapting, fine-tuning, or optimizing generative models, including open-source LLMs
• Strong understanding of modern LLM system design patterns, including retrieval, tool use, context engineering, evaluation, and agentic workflow orchestration
• Experience with containerization, Docker, Kubernetes, cloud platforms, and CI/CD pipelines
• Experience with at least one orchestration framework or platform for building LLM-based applications
• Familiarity with MLOps practices, including model monitoring, experiment tracking, evaluation pipelines, and production model lifecycle management
• Ability to make sound technical decisions considering scalability, reliability, performance, security, and cost
• Comfortable working with AI-assisted software development workflows and using modern coding agents to accelerate planning, implementation, testing, and iteration
• Strong collaboration and communication skills, with the ability to work effectively across product, engineering, and business teams

Nice to Have

• Contributions to open-source AI projects
• Expertise in LLM evaluation, guardrails, observability, and performance-cost optimization
• Experience with frameworks such as LangGraph, GoogleADK, or similar tools
• Experience with multimodal AI, graph-based AI systems, reinforcement learning, or other advanced AI domains
• Experience designing AI systems for enterprise-scale use cases
• Active engagement in AI communities such as Kaggle, Hugging Face, or similar platforms
• Experience mentoring engineers or leading technical initiatives in AI/ML teams