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We’re looking for an AI Engineer who has both depth and breadth in the AI space. While you’ll bring strong hands-on expertise in GenAI, we don’t believe every problem needs a GenAI hammer. You should have the judgment to choose the right approach, whether it’s NLP, computer vision, classical ML, or data pipelines, and the curiosity to keep learning as new technologies emerge. This role is about building practical, scalable AI solutions that solve real business problems. You’ll work across the entire lifecycle, from data preparation to deployment, while staying close to the business context and ensuring the solutions we build are impactful, not just experimental. We also value people who are proactive, willing to take ownership, and ready to drive initiatives that go beyond day-to-day coding.
Job Responsibility:
Designing, building, and integrating AI models with a focus on scalability, efficiency, and adaptability
Supporting MLOps practices to help models move from research to reliable production systems
Handling key parts of data preparation: cleaning, preprocessing, transformation, and validation
Writing maintainable, high-quality code with reusability in mind
Building APIs, pipelines, and prototypes that make AI solutions tangible and useful
Collaborating with AI engineers, developers, and product owners to turn ideas into working systems
Taking initiative in projects, contributing ideas, driving small experiments, and helping shape how we build solutions
Staying current with the fast-moving AI ecosystem, GenAI and beyond, and applying approaches that fit the problem
Requirements:
Solid foundation in AI/ML, with hands-on exposure to developing, integrating, and evaluating GenAI models
Familiarity with multiple AI domains such as NLP, computer vision, recommendation systems, classical ML
Experience with one or more ML/DL frameworks: PyTorch, TensorFlow, or SciKit-learn
Exposure to agentic AI systems (frameworks like LangChain, LlamaIndex, CrewAI, Agno) and evaluation of GenAI systems
Strong data handling skills (exploration, cleaning, preprocessing)
Ability to prototype quickly and build simple APIs to demonstrate solutions
Problem-solving mindset and communication skills, you should be able to explain not just how something works but also why it matters
Nice to have:
Experience applying AI to real-world projects (academic, internship, or professional)
Curiosity and adaptability to explore new tools, frameworks, and problem domains