Platform Engineer I

Role overview

Responsibilities

• check_circle Design, fine-tune, and deploy large language models (LLMs) and other generative models for specific business use cases.
• check_circle Build and optimize prompt engineering and model interaction strategies for improved output accuracy and relevance.
• check_circle Develop APIs, pipelines, and integrations to embed generative AI capabilities into products and workflows.
• check_circle Collaborate with data engineers to ensure high-quality, domain-relevant datasets for model training and evaluation.
• check_circle Implement responsible AI principles, including bias mitigation, explainability, and data governance.
• check_circle Monitor model performance and continuously refine models through feedback loops and retraining.
• check_circle Stay up to date with emerging trends in AI research, frameworks (e.g., LangChain, Hugging Face, OpenAI API), and open-source tools.
• check_circle Contribute to internal knowledge sharing and best practices for scalable AI deployment.

Basic qualifications

• Proficiency in Python and frameworks such as PyTorch or TensorFlow.
• Experience in SQL, Machine Learning, Big Dat and other Databases.
• Experience with LLM APIs (OpenAI, Anthropic, Gemini, etc.) and vector databases (Pinecone, FAISS, Weaviate).
• Familiarity with prompt engineering, model fine-tuning, and reinforcement learning from human feedback (RLHF).
• Strong understanding of NLP, deep learning, and data pipeline design.
• Exposure to cloud platforms (AWS, Azure, GCP) and MLOps tools for model deployment.
• Strong problem-solving and analytical thinking.

Preferred qualifications

• Certifications in GenAI or Agentic AI