AI & Machine Learning Engineer

AI Engineer – Computer Vision, NLP & Deep Learning

Type, Location,

Full Time @ Pune

Desired Experience

4+ years

Job Description

Role

• Develop, fine-tune, and deploy deep learning models for computer vision and NLP use cases using PyTorch or TensorFlow.
• Design and maintain model pipelines, data loaders, and API layers in Python for scalable inference and integration.
• Implement frontend and backend integrations of AI models using TypeScript/JavaScript (e.g., Node.js, LangChain.js, Transformers.js).
• Work with datasets for annotation, augmentation, and visualization using tools like LabelImg, Albumentations, and FiftyOne.
• Build semantic search and recommendation systems using vector databases like Pinecone, Weaviate, or Milvus.
• Integrate NoSQL and graph-based storage systems like MongoDB and Neo4j for AI-related data operations.
• Collaborate with cross-functional teams to deliver production-ready, observable, and testable ML components.
• Contribute to infrastructure and CI/CD for AI model deployment and versioning.
• Document architectures, APIs, model behavior, and performance tuning guidelines.
• Participate in sprint planning, reviews, and architecture discussions within a remote-first engineering team.

Qualifications

• 5+ years of experience in AI/ML engineering with deep expertise in computer vision, NLP, and model lifecycle management.
• Advanced proficiency in Python for deep learning, data pipelines, and API development.
• Hands-on experience with PyTorch or TensorFlow, with strong skills in training, fine-tuning, and optimizing DL models.
• Experience in TypeScript/JavaScript for integrating AI models into web applications using Node.js or frontend frameworks.
• Familiarity with C++ for performance-critical or system-level tasks is a plus.
• Experience working with vision tools (OpenCV, Detectron2/MMDetection) and managing large annotated datasets.
• Strong understanding of NoSQL (MongoDB, Redis), vector databases (Pinecone, Milvus), and graph DBs (Neo4j).
• Proficiency in building scalable ML services, with observability (logging, metrics, alerting) and test coverage.
• Exposure to orchestration tools (Airflow, Prefect) and cloud deployment workflows (Azure preferred).
• Comfortable working in remote, agile teams with strong communication and problem-solving skills.
• Ability to work independently and drive AI projects from experimentation to production.