Data Science GenAI Engineer

4–6 years of overall software engineering or ML experience.

2+ years of hands-on experience in Generative AI, LLMs, or applied NLP.

Strong programming skills in Python (required); experience with JavaScript/TypeScript is a plus.

Practical experience with:

• LLM APIs and frameworks (e.g., OpenAI-compatible APIs, LangChain, LlamaIndex)
• Prompt engineering and RAG architectures
• Vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma)

Experience deploying ML/AI solutions in cloud environments (AWS, Azure, or GCP).

Solid understanding of data structures, APIs, and system design.

Ability to write clean, maintainable, and testable code.

Design, develop, and deploy Generative AI applications using LLMs (e.g., GPT-based, open-source models).

Build and optimize prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning pipelines.

Integrate GenAI models into production systems using APIs, microservices, and cloud platforms.

Collaborate with product managers, data scientists, and backend engineers to translate business needs into AI-driven solutions.

Evaluate model performance, mitigate hallucinations, and improve accuracy, safety, and latency.

Implement monitoring, logging, and feedback loops for continuous model improvement.

Stay current with emerging GenAI frameworks, tools, and best practices.

Ensure compliance with security, privacy, and responsible AI guidelines.

Experience in RAG based applications using LangGraph and FastAPI.

Familiarity with MLOps practices (CI/CD for ML, model versioning, monitoring).

Knowledge of NLP concepts such as embeddings, transformers, and tokenization.