Experience: 2+ Years
Location: Bengaluru / On-site
Department: Artificial Intelligence
We are looking for a hands-on AI Engineer with 2+ years of experience to design, build, and deploy production-grade AI systems, with strong focus areas in Generative AI / LLMs, OCR-based document/data extraction, and Computer Vision (classification, object detection, segmentation). The ideal candidate is comfortable working across the full stack — from model development/integration to backend APIs to deployment.
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Design, develop, and deploy GenAI/LLM-based solutions (RAG pipelines, prompt engineering, fine-tuning, agentic workflows).
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Build and optimize OCR pipelines for extracting structured data from scanned documents, PDFs, images, and forms.
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Develop, train, and fine-tune Computer Vision models for image classification, object detection, and segmentation tasks.
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Develop and maintain RESTful APIs using FastAPI to expose AI/ML models and services.
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Containerize applications using Docker and manage deployment across environments.
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Design and manage data storage using MongoDB (NoSQL) and SQL databases.
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Integrate LLM APIs (OpenAI, Anthropic Claude, open-source LLMs like LLaMA/Mistral) into production applications.
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Work with vector databases (FAISS, Pinecone, Chroma, Weaviate, etc.) for semantic search and RAG implementations.
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Collaborate with cross-functional teams (product, backend, DevOps) to integrate AI features into existing platforms.
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Write clean, maintainable, well-documented, and testable code.
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Monitor model performance, debug issues, and iterate on solutions based on production feedback.
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Stay current with advancements in GenAI, LLMs, OCR, and Computer Vision.
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2+ years of experience in AI/ML engineering.
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Strong proficiency in Python.
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Hands-on experience with GenAI / LLMs — prompt engineering, RAG, embeddings, fine-tuning, or agentic frameworks (LangChain, LlamaIndex, LangGraph or similar).
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Practical experience with OCR technologies (Tesseract, PaddleOCR, AWS Textract, Google Vision OCR, Azure Form Recognizer, or similar) for document/image data extraction.
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Hands-on experience with Computer Vision — image classification, object detection (YOLO, Faster R-CNN, etc.), and segmentation (U-Net, Mask R-CNN, semantic/instance segmentation) using frameworks like PyTorch, TensorFlow, or OpenCV.
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Solid experience building APIs with FastAPI.
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Working knowledge of Docker — building images, writing Dockerfiles, docker-compose.
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Experience with MongoDB and SQL databases (schema design, queries, indexing, aggregation).
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Understanding of core ML/DL concepts (CNNs, transformers, embeddings, evaluation metrics like IoU, mAP, F1).
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Familiarity with version control (Git) and basic CI/CD practices.
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Good understanding of REST API design principles and asynchronous programming in Python.
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Experience with vector databases and semantic search.
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Exposure to cloud platforms (AWS/Azure/GCP), especially their AI/ML and storage services.
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Experience with model training/fine-tuning pipelines (Hugging Face, PyTorch, Detectron2, MMDetection).
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Familiarity with medical imaging formats (DICOM) or other domain-specific imaging pipelines.
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Familiarity with message queues (RabbitMQ/Kafka) for async processing.
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Experience with monitoring/logging tools for production AI systems.
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Prior experience in healthcare, fintech, or document-heavy domains is a plus.
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Strong problem-solving ability and willingness to work across the stack.
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Ability to work independently and in a fast-paced, iterative environment.
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Good communication skills to collaborate with cross-functional teams.
