AI Engineer

·      Minimum 4+ years of hands-on experience developing AI-based applications using Python.

·      Experience using deep learning frameworks (TensorFlow, PyTorch, or Keras)

·      Experience in Pydantic

·      Solid understanding of ML/AI concepts: EDA, preprocessing, algorithm selection, machine learning frameworks, model efficiency metrics, model monitoring.

·      Familiarity with Natural Language Processing (NLP) techniques.

·      Deep understanding of Large Language Models (LLM) Architectures, their capabilities and limitations.

·      Proven expertise in integrating and working with LLMs to build robust AI solutions.

·      Skilled in crafting effective prompts to guide LLMs to provide desired outputs.

·      Hands-on experience with LLM frameworks such as Langchain,Langraph,CrewAI etc.,

·      Experience in LLM application development based on Retrieval-Augmented Generation (RAG) concept, familiarity with vector databases, and fine-tuning large language models (LLMs) to enhance performance and accuracy.

·      Proficient in microservices development using Python (Django/Flask or similar technologies).

·      Excellent communication skills with the ability to effectively collaborate with technical and non-technical stakeholders

·      Experience in MLOPs frameworks (BentoML,Kubeflow or similar  technologies) and exposure to LLMOPs

·      Experience with end-to-end chatbot development, including design, deployment, and ongoing optimization, leveraging NLP and integrating with backend systems and APIs.

·      Understanding of security and ethical best practices for data and model development

·      Contributions to open source projects

·      Experience in Agile development methodologies

·      Familiarity with platforms like Kubernetes and experience building on top of the native platforms

·      Experience with cloud-based data platforms and services (e.g., IBM, AWS, Azure, Google Cloud).

·      Experience designing, building, and maintaining data processing systems working in containerized environments (Docker, OpenShift, k8s)