India: Intern/Trainee (Software Engineer)

We are looking for a passionate AI/ML Developer to join our team and contribute to building next-generation AI applications. You will work on machine learning models, Generative AI systems, RAG (Retrieval-Augmented Generation) pipelines, and Agentic AI frameworks, leveraging cloud platforms such as Azure AI (or similar) to develop scalable, production-ready solutions.

This role is ideal for someone who has hands-on experience in Python, AI/ML frameworks, and modern GenAI techniques and is excited about applying them to solve real-world business problems.

Key Responsibilities

• Design, build, and deploy AI/ML models for classification, prediction, and optimization tasks.

• Develop and implement GenAI solutions (LLMs, prompt engineering, fine-tuning, embeddings).

• Build RAG pipelines integrating vector databases, knowledge graphs, and LLMs.

• Implement Agentic AI workflows for automation, reasoning, and multi-step task execution.

• Work with Azure AI services (OpenAI, Cognitive Search, ML Studio, Data Factory) or similar platforms (AWS Sagemaker, GCP Vertex AI).

• Optimize ML pipelines for scalability, performance, and cost efficiency in cloud and edge environments.

• Collaborate with cross-functional teams (data engineers, domain experts, product managers).

• Stay updated with the latest AI/ML/GenAI research and tools and integrate best practices.

Required Skills & Qualifications

• Education: B.E./B.Tech/M.Tech in Computer Science, AI/ML, Data Science, or related field.

• Experience: 2–5 years in AI/ML development.

• Programming: Strong in Python (NumPy, Pandas, PyTorch/TensorFlow, LangChain, Transformers).

• ML/AI: Model training, fine-tuning, evaluation, deployment.

• GenAI: Experience with LLMs (OpenAI, LLaMA, Mistral, etc.), embeddings, and prompt design.

• RAG Systems: Knowledge of vector databases (Pinecone, FAISS, Weaviate, Milvus).

• Agentic AI: Familiarity with AI agents (LangChain Agents, AutoGen, CrewAI, Semantic Kernel).

• Cloud: Hands-on with Azure AI (preferred) or AWS/GCP AI services.

• MLOps: Model versioning, CI/CD, deployment using MLflow/Docker/Kubernetes.

• Strong problem-solving, analytical, and debugging skills.

Good to Have

• Knowledge of NLP, Computer Vision, or Multi-modal AI.

• Experience with knowledge graphs or graph databases.

• Exposure to edge AI deployments.

• Contributions to open-source AI/ML projects.