Key Responsibilities
Design, implement, and optimize AI/ML models for production use.
Work on Generative AI (LLMs, diffusion models, transformers, etc.) to build scalable
solutions.
Develop and integrate RAG pipelines for intelligent information retrieval.
Implement and optimize Vector Databases (Pinecone, Weaviate, FAISS, Milvus, etc.) for
semantic search and storage.
Create and fine-tune embeddings for text, images, and multi-modal tasks.
Develop and integrate Agentic AI systems with tool orchestration, planning, and
reasoning.
Work with MCP (Model Context Protocol) for secure and structured AI integrations.
Collaborate with product, data, and engineering teams to deliver robust AI solutions.
Stay up-to-date with the latest AI/ML research, frameworks, and industry trends.
Required Skills & Qualifications
Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or related field.
2–3 years of hands-on experience in AI/ML engineering.
Strong understanding of machine learning algorithms, deep learning, and NLP.
Experience with Gen AI frameworks (LangChain, LlamaIndex, Haystack, etc.).
Practical knowledge of RAG architecture and pipeline design.
Hands-on experience with Vector Databases (e.g., Pinecone, FAISS, Weaviate, Milvus).
Strong skills in Python, PyTorch, TensorFlow, or Hugging Face Transformers.
Familiarity with Agentic AI frameworks (LangGraph, AutoGPT, CrewAI, etc.).
Experience in MCP (Model Context Protocol) and secure LLM integrations.
Knowledge of cloud platforms (AWS, GCP, Azure) and deployment practices.
Strong problem-solving skills, analytical thinking, and communication abilities.
Preferred Skills (Nice to Have)
Exposure to MLOps (MLflow, Kubeflow, Vertex AI, SageMaker).
Experience in multi-modal AI (text, image, audio).
Knowledge of containerization (Docker, Kubernetes).
Understanding of security best practices in AI/ML systems.
Contributions to open-source AI/ML projects.
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