Generative AI Engineer

Key skills: Python, LLM, and RAG ( Good work voice model)

It's an early startup, so 6 days of work

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 techniques