Artificial Intelligence Engineer - LLM Models

Role Overview :

We are seeking a highly skilled AI R&D Engineer with expertise in large language models, startup dynamics, and building production-grade AI products. You will play a pivotal role in designing, implementing, and optimizing LLM-driven systems that power our next-generation conversational AI solutions.

Key Responsibilities :

- Design, train, fine-tune, and evaluate large language models for diverse conversational AI use cases.

- Stay updated on the latest advancements in LLMs, NLP, and generative AI.

- Propose and implement innovative methods, tools, and frameworks to enhance model performance, efficiency, and reliability.

- Collaborate with product managers, software engineers, and UX designers to seamlessly integrate AI models into user-facing applications.

- Develop APIs, tools, and infrastructure for deploying models in production environments.

- Establish robust evaluation metrics, benchmarks, and automated testing frameworks for conversational systems.

- Continuously monitor model performance and iterate based on user feedback and data-driven insights.

- Guide and mentor junior engineers in applied AI research, engineering best practices, and MLOps.

- Foster a culture of experimentation, excellence, and continuous learning.

Qualifications & Experience :

Core Requirements :

- At least 2 years of experience in AI/ML R&D, with a proven track record in working with state-of-the-art NLP and LLM technologies (e.g., GPT, LLaMA, Falcon, PaLM).

- Prior experience in fast-paced, high-growth environments with iterative development and evolving priorities.

- Strong programming skills in Python.

- Proficiency with ML frameworks like PyTorch or TensorFlow.

- Experience with cloud platforms (AWS, GCP, Azure), containerization (Docker), and CI/CD pipelines.

- Expertise in fine-tuning LLMs, prompt engineering, and building scalable data pipelines.

- Ability to effectively communicate complex ideas to technical and non-technical stakeholders while thriving in cross-functional teams.

Bonus Points :

- Contributions to open-source AI/ML/NLP projects.

- Experience with vector databases, retrieval-augmented generation (RAG), or multimodal models.

- Familiarity with MLOps tools (e.g., MLflow, Kubeflow) and distributed training techni