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AI/ML Engineer Experience : 3+ years of overall experience Primary Skills (Must Have) : - AI - ML - Python - NLP - RAG - OCR - Deep Learning - Computer Vision - Vector DB, AWS/Azure Location : Hyderabad (Hybrid) Shift (IST Hours) : General Shift Job Description : We are looking for a skilled AI/ML Engineer to join our team in Hyderabad. You will design, build, and deploy intelligent systems spanning NLP, Computer Vision, OCR, Deep Learning, and RAG-based retrieval pipelines. You will own the full lifecycle from model development to production deployment on AWS or Azure. Key Responsibilities : - Design and develop end-to-end ML pipelines using Python for training, evaluation, and production deployment. - Build NLP solutions including text classification, named entity recognition (NER), summarisation, semantic search, and question answering using transformer models such as BERT, RoBERTa, and T5. - Implement OCR pipelines using Tesseract, PaddleOCR, or EasyOCR for intelligent document processing and extraction from unstructured sources (PDFs, scanned files, images). - Build and fine-tune deep learning models using TensorFlow or PyTorch, including CNNs, RNNs, and Transformer architectures. - Develop computer vision models for image classification, object detection, image segmentation, and visual recognition tasks. - Design and maintain RAG pipelines covering document ingestion, chunking, embedding generation, and retrieval optimisation using vector databases. - Work with vector databases such as Pinecone, Weaviate, FAISS, or ChromaDB for semantic search and knowledge retrieval. - Deploy and manage AI/ML models on AWS (SageMaker, EC2, Lambda, S3) or Azure (Azure ML, Cognitive Services). - Build and expose ML models as REST APIs using FastAPI or Flask for downstream integration. - Track experiments, manage model versioning, and monitor drift using MLflow, Weights and Biases, or similar MLOps tools. - Containerise and orchestrate ML workloads using Docker and Kubernetes for scalable model serving. - Collaborate with product, backend, and data engineering teams to integrate AI solutions into production systems. Required Skills and Experience : - 3+ years of hands-on experience in AI/ML engineering in a production environment. - Strong Python skills with proficiency in ML libraries : PyTorch, TensorFlow, Scikit-learn, NumPy, and Pandas. - Solid NLP experience including text preprocessing, tokenisation, and transformer-based model development and deployment. - Hands-on experience with OCR tools such as Tesseract, PaddleOCR, or EasyOCR and handling varied unstructured document formats. - Deep learning expertise with CNNs, RNNs, and Transformers across classification, detection, and generation tasks. - Proficiency in computer vision tasks: object detection (YOLO, Faster R-CNN), image classification, and segmentation. - Working knowledge of RAG pipeline components: document ingestion, chunking strategies, embedding models, and retrieval tuning. - Hands-on experience with vector databases: Pinecone, Weaviate, FAISS, ChromaDB, or Milvus. - Cloud deployment experience on AWS (SageMaker, Lambda, EC2, S3) or Azure (Azure ML, Cognitive Services). - Familiarity with containerization tools: Docker, Kubernetes, and CI/CD practices for ML workflows. - Understanding of MLOps concepts including model monitoring, retraining pipelines, and experiment tracking. - Ability to build scalable REST APIs using FastAPI or Flask to serve ML models. Good to Have : - Exposure to GenAI or LLM-based development using frameworks such as LangChain or LlamaIndex. - Experience working with managed LLM APIs such as Azure OpenAI Service or AWS Bedrock. - Familiarity with LLM fine-tuning techniques including LoRA, QLoRA, or RLHF. - Knowledge of agentic AI systems or multi-agent workflows. - Familiarity with evaluation frameworks such as RAGAS and DeepEval for RAG benchmarking. - Exposure to multimodal models that combine vision and language, such as CLIP or BLIP. - Understanding of model quantisation and inference optimisation techniques (ONNX, TensorRT, vLLM). Qualifications : - Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related engineering field. - 3+ years of industry experience in applied ML/AI with demonstrated production deployments. - Strong analytical and problem-solving skills with the ability to work independently. - Good communication skills to work effectively with both technical and non-technical stakeholders. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.