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