About the Role
We are looking for an AI/ML Engineer with hands-on experience in Generative AI, Large Language Models (LLMs), NLP, and Retrieval-Augmented Generation (RAG) to design, build, and deploy enterprise-grade AI solutions. The ideal candidate will have strong foundations in Python-based ML development, vector databases, prompt engineering, LLM orchestration, and the Azure AI ecosystem.
Responsibilities
- Generative AI & LLM Engineering
- Build and fine-tune LLM-based applications using models such as GPT, LLaMA, Mistral, Phi, etc.
- Develop custom pipelines for RAG, grounding LLMs on enterprise documents (PDFs, Word, PPT, HTML, images).
- Design robust prompt engineering strategies—system prompts, few-shot examples, evaluation prompts.
- Implement guardrails using safety filters, grounding validation, and hallucination detection.
- NLP Model Development
- Develop and optimize NLP models for text classification, entity extraction, summarization, Q&A, topic modelling, and semantic search.
- Apply transformer-based architectures (BERT, RoBERTa, T5, LLaMA-based models).
- Build multilingual NLP pipelines where required.
- RAG Pipelines & Knowledge Engineering
- Create scalable RAG architectures using:
- Vector databases (Azure AI Search, Pinecone, Weaviate, FAISS).
- Chunking, metadata tagging, hybrid search, and document embeddings.
- Build connectors for ingestion pipelines (ETL) for structured/unstructured sources.
- Evaluate and optimize retrieval quality (R@K, MRR, context relevancy scores).
- Azure AI & MLOps
- Use Azure AI Search, Azure OpenAI, Azure Machine Learning, Azure Functions, Data Lake, and DevOps pipelines to build end-to-end solutions.
- Deploy LLM-based APIs, microservices, and inference endpoints using AKS/ACI.
- Implement model evaluation frameworks, monitoring (logging, latency, cost), and lifecycle management.
- Software Engineering & API Development
- Build Python-based backend services for LLM inference, embeddings, and RAG operations.
- Develop REST APIs and integrate with downstream applications.
- (Nice-to-Have) Build simple UI front-ends using React for demos and internal tools.
- Collaboration & Documentation
- Work with domain SMEs, product teams, and engineering to convert business needs into AI use cases.
- Document model architectures, design decisions, and best practices.
Qualifications
Required Skills & Qualifications
- 3–5 years of experience in AI/ML, with at least 1–2 years in Generative AI/LLMs/NLP.
- Strong proficiency in Python, PyTorch, Hugging Face Transformers, LangChain/LlamaIndex.
- Hands-on experience with RAG architectures, vector stores, and embedding models.
- Strong understanding of transformer architecture, tokenization, embeddings, and LLM evaluation metrics.
- Experience with Azure AI stack—Azure OpenAI, Cognitive Search/AI Search, Azure ML, Data Lake.
- Familiarity with MLOps tools for tracking, deployment, CI/CD.
- Experience working with unstructured data at scale—documents, text, OCR extractions.
Nice-to-Have Skills
- Experience with React.js for building AI dashboards and internal apps.
- Exposure to knowledge graphs, ontology/metadata design, and structured knowledge retrieval.
- Experience deploying LLMs on edge or private environments (ONNX Runtime, quantized models).
- Familiarity with evaluation frameworks such as Ragas, DeepEval, or custom LLM eval pipelines.
Education
- Bachelor’s/Master’s degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent fields.