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 : Hyderabad(Hybrid)

Mandatory Skill : AI/ML+GenAi+LLM+NLP+Python(Rag, Flora-Optional)

Key Qualifications :

- 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.

Key Responsibilities :

- 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.