GenAI/ML Engineer - Python/LLM

Job Overview:

We are looking for an experienced GenAI/ML Engineer to design, develop, and deploy machine learning and Generative AI solutions. The ideal candidate should have strong expertise in Generative AI, Large Language Models (LLMs), Python, machine learning, RAG, and AI/ML frameworks.

The candidate will work on building intelligent applications and scalable AI solutions that solve complex business problems.

Key Responsibilities:

- Design, develop, and deploy Generative AI and Machine Learning solutions.

- Develop applications using Large Language Models (LLMs) and foundation models.

- Build Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases.

- Develop and optimize prompts for LLM-based applications.

- Work with embeddings, vector databases, semantic search, and document retrieval.

- Develop machine learning models and integrate them into production applications.

- Perform data preparation, feature engineering, model training, evaluation, and optimization.

- Implement AI/ML solutions using Python and relevant ML/AI frameworks.

- Integrate LLMs and AI services through APIs into enterprise applications.

- Evaluate model performance, accuracy, latency, and cost.

- Implement techniques for improving LLM responses, including prompt engineering and retrieval optimization.

- Collaborate with data scientists, software engineers, architects, and business stakeholders.

- Deploy and monitor AI/ML models and applications in cloud or production environments.

- Troubleshoot model and application issues and continuously improve AI solutions.

- Stay updated with emerging developments in Generative AI, LLMs, and Machine Learning.

Required Skills:

- Strong experience in Generative AI / Machine Learning.

- Strong programming skills in Python.

- Hands-on experience with LLMs and Generative AI applications.

- Experience building RAG-based applications.

- Strong understanding of Prompt Engineering.

- Experience with machine learning and deep learning concepts.

- Hands-on experience with frameworks such as PyTorch, TensorFlow, LangChain, or LlamaIndex.

- Experience working with embeddings and vector databases.

- Knowledge of NLP and transformer-based architectures.

- Experience with REST APIs and integrating AI models into applications.

- Strong understanding of model evaluation and performance optimization.

- Good problem-solving and analytical skills.