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