Posted on: 13/10/2025
Description :
- Build and integrate REST/GraphQL APIs to connect LLMs with enterprise systems.
- Develop, refine, and implement prompt engineering techniques to optimize LLM performance and accuracy.
- Perform data preprocessing and create high-quality embeddings to support AI/ML pipelines.
- Evaluate LLM performance in terms of accuracy, latency, and cost; recommend and implement optimizations.
- Support fine-tuning and model customization to align with specific enterprise requirements and use cases.
- Work with vector databases to support efficient semantic search and data retrieval.
- Ensure compliance with AI safety, security, and ethical considerations, including hallucination mitigation, bias detection, and data privacy.
Primary Skills :
- LLM Experience : Practical experience with LLMs (e.g., OpenAI GPT, Claude, Mistral) including integration, tuning, and evaluation.
- Prompt Engineering : Deep understanding of prompt design and optimization strategies.
- API Development : Experience with REST and GraphQL API integration.
- Data Engineering : Proficiency in data preprocessing, manipulation, and transformation for AI use cases.
Secondary Skills :
- Model Fine-tuning : Experience in customizing and fine-tuning open-source or proprietary LLMs.
- Performance Evaluation : Ability to measure and optimize LLM performance (latency, accuracy, and cost).
- Vector Databases : Familiarity with tools like FAISS, Pinecone, Weaviate, or similar.
Good to Have Skills :
- Cloud Platforms : Exposure to deploying AI solutions on cloud platforms such as AWS, GCP, or Azure.
- DevOps Practices : Familiarity with CI/CD pipelines and containerization (Docker/Kubernetes).
- Visualization Tools : Ability to present data and model insights using dashboards (e.g., Streamlit, Dash, or similar).
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