Description :

Job Description :

We are looking for a highly skilled Gen AI Developer with hands-on experience in building AI-driven applications using the latest evolving technologies.

The ideal candidate will have expertise in LLMs, model training, chatbot implementation, and AI workflow development while working with cloud platforms like AWS, Azure, and OpenAI.

Key Responsibilities :

- Develop, fine-tune, and deploy Large Language Models (LLMs) for various use cases.

- Build and optimize AI/ML workflows using tools like Azure AI/ML Studio, AWS Bedrock, and OpenAI APIs.

- Implement chatbots and AI-driven applications with frameworks like LangChain and Hugging Face.

- Train and fine-tune models for NLP, text generation, embeddings, and multimodal AI applications.

- Work with vector databases, embeddings, and prompt engineering for AI-powered search and recommendations.

- Design and deploy AI/ML solutions on AWS, Azure, and other cloud platforms.

- Integrate AI models into production environments and optimize them for scalability and efficiency.

- Collaborate with cross-functional teams, including data engineers and software developers, to enhance AI capabilities.

- Stay updated with the latest trends in AI/ML, Gen AI, and cloud AI services.

Required Skills :

- Strong programming skills in Python with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face).

- Experience with LangChain, OpenAI APIs, Hugging Face Transformers.

- Hands-on expertise in training and fine-tuning LLMs (GPT-4, Falcon, Llama, Claude, Mistral, etc.

- Experience in deploying AI models on AWS (SageMaker, Bedrock, Lambda) and Azure (AI/ML Studio, OpenAI services).

- Knowledge of RAG (Retrieval-Augmented Generation) and vector databases (Pinecone, FAISS, LanceDB, Redis Stack).

- Experience with MLOps, model monitoring, and continuous training pipelines.

- Understanding of cloud-native architectures, APIs, microservices, and containerization (Docker, Kubernetes).

- Strong problem-solving and analytical skills.

Preferred Skills :

- Experience with multi-modal AI (text, image, voice models).

- Exposure to serverless AI architectures.

- Familiarity with data engineering for AI pipelines.

- Experience in secure and responsible AI practices