About Role

We are seeking a skilled Generative AI Developer with hands-on expertise in designing, fine-tuning, and deploying LLM (Large Language Model)-based solutions. The ideal candidate will bring deep technical knowledge in AI/ML along with strong software engineering skills to build scalable, production-ready applications powered by Generative AI. You will work closely with data scientists, ML engineers, and product teams to deliver innovative solutions across multiple domains.

Role and Responsibilities

Develop and deploy Generative AI applications using LLMs, diffusion models, and other foundation models.
Fine-tune pre-trained models (GPT, LLaMA, Falcon, Mistral, etc.) for domain-specific business use cases.
Implement RAG (Retrieval-Augmented Generation) pipelines with vector databases such as FAISS, Pinecone, Weaviate, or Milvus.
Build APIs and microservices to integrate GenAI solutions into enterprise applications.
Optimize model performance for scalability, latency, and cost efficiency.
Collaborate with data engineers on ETL/ELT pipelines and preparation of unstructured/semi-structured data.
Apply prompt engineering and prompt-tuning techniques to improve model accuracy and contextual responses.
Ensure responsible AI usage by implementing bias mitigation, compliance, and security best practices.
Stay updated with the latest research and trends in Generative AI and applied ML.

Qualification Required

Education: Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Data Science, or related field.

Programming: Strong expertise in Python and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex.
Generative AI/LLMs: Hands-on experience training, fine-tuning, and deploying LLMs (GPT, LLaMA, etc.) and working with embeddings.
MLOps/Deployment: Experience with cloud platforms (Azure, AWS, GCP), Docker, Kubernetes, and CI/CD pipelines for ML deployment.
Vector Databases: Knowledge of FAISS, Pinecone, Weaviate, or Milvus.
Data Handling: Strong experience with data preprocessing, data lakes, and pipelines.
Cloud AI Services: Familiarity with Azure OpenAI, AWS Bedrock, GCP Vertex AI, or equivalents.
Software Engineering: Understanding of version control, testing, APIs, and Agile development practices.

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