Unify Technologies - Senior Engineer Lead Architect - Machine Learning Artificial Intelligence

Job Role/Title : Senior/ Lead /Architect SDE-ML/AI Engineer

Experience : Senior: 4-15 years

Location : Skill : AI/ML+GenAi+LLM+NLP+Python(Rag, Qualifications :

• Minimum 5 years of experience in AI/ML with at least 2+ years in NLP, LLMs, and Generative AI.
• Proven expertise in ML architecture design, end-to-end model development, and deployment in production systems.
• Strong in Python with deep experience in ML libraries and frameworks such as TensorFlow, PyTorch, Hugging Face, and LangChain.
• Sound knowledge of transformer models, embeddings, tokenization, and vector databases (e.g., FAISS, Pinecone).
• Experience with cloud-native AI solutions on AWS, Azure, or GCP.
• Familiarity with MLOps, model versioning, containerization (Docker), and orchestration tools (e.g., Kubeflow, MLflow).
• Hands-on experience in designing and engineering prompts for LLMs to support use cases like summarization, classification, Q&A, and content generation.
• Strong understanding of retrieval-augmented generation (RAG) and techniques to combine structured/unstructured data with LLMs.
• Excellent problem-solving skills, architectural thinking, and ability to lead complex AI initiatives.
• Strong communication, stakeholder management, and technical leadership capabilities.
  
  

• Architect and implement end-to-end machine learning and Generative AI solutions for real-world applications.
• Design, fine-tune, and deploy models using transformers, embeddings, tokenization, and LLMs for tasks such as summarization, classification, question answering, and content generation.
• Develop and maintain high-quality, production-grade ML code in Python, using libraries like TensorFlow, PyTorch, Hugging Face, LangChain, etc.
• Build and optimize retrieval-augmented generation (RAG) pipelines by integrating LLMs with structured and unstructured data.
• Work with vector databases (e.g., FAISS, Pinecone) to manage semantic search and context retrieval efficiently.
• Utilize cloud-native AI services (AWS, GCP, Azure) for model training, deployment, and scaling.
• Implement MLOps best practices, including model versioning, containerization (Docker), orchestration (Kubeflow, MLflow), and CI/CD.