- 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.