Company Description

At TalentRank, we’re on a mission to revolutionize hiring. 🚀

We leverage cutting-edge AI technology to streamline recruitment with solutions that are efficient, unbiased, and scalable.

Our AI interviewer doesn’t just save time — it empowers companies to discover top talent with speed and precision. ⚡

No more endless CV reviews. Just smarter decisions, faster hires, and a fairer hiring process.

Join us as we build the future of hiring. 💼✨

Role Description

This is a full-time AI Engineer position with full remote flexibility.

You’ll be at the heart of our AI innovation efforts, leading the design and development of large language model (LLM) and multimodal solutions for our next-generation interview platform. From fine-tuning models to deploying robust AI pipelines, you’ll help shape how AI evaluates and empowers human potential in hiring.

Key Responsibilities

• Design, build, and optimize production-grade LLMs and multimodal AI solutions (text, vision, speech)
• Lead full-cycle AI development: data pipelines, model training, evaluation, deployment, and monitoring
• Collaborate with product and software engineering teams to integrate generative AI into enterprise-grade solutions
• Drive proof-of-concept projects and explore cutting-edge research and architectures
• Implement MLOps practices using tools like MLflow, Docker, Kubernetes, and cloud services (AWS/GCP/Azure)
• Uphold ethical AI practices—ensuring fairness, explainability, and security in our models

Qualifications

• BSc/MSc/PhD in Computer Science, AI, or a related field
• Strong proficiency in Python and ML frameworks like PyTorch or TensorFlow
• Experience with transformer-based architectures and libraries (Hugging Face, spaCy, etc.)
• Proven track record in fine-tuning and deploying LLMs (e.g., GPT, LLaMA) or generative models (e.g., diffusion models)
• Familiarity with containerization (Docker), orchestration (Airflow, Kubeflow), and distributed computing (Spark, Dask)
• Cloud experience (AWS, Azure, or GCP) and working knowledge of modern MLOps workflows

Nice to Have

• Contributions to open-source AI libraries or publications in AI/ML conferences
• Experience with prompt engineering, LLM evaluation metrics, or cost-performance optimization
• Exposure to advanced domains such as graph neural networks or reinforcement learning
• Active engagement in the AI community (e.g., GitHub, arXiv, Kaggle)

What We Offer

• Opportunity to shape the AI backbone of a real-world SaaS product used in hiring processes
• Remote working model
• Hands-on involvement in both research and production-grade AI deployments
• Collaborative and innovation-driven startup environment
• Ownership, autonomy, and rapid iteration cycles