Senior Artificial Intelligence Engineer - Python Programming (6-8 yrs)

We are looking for an experienced Senior AI Engineer with a strong background in Python programming and extensive experience with Generative AI and Large Language Models (LLMs) to join our dynamic team.

In this role, you will lead the development and deployment of cutting-edge AI solutions, focusing on Retrieval-Augmented Generation (RAG) techniques, LLMs, and vector databases, including tools like Weaviate and Pinecone.

You will work on building scalable applications that leverage advanced AI techniques and collaborate with both technical and non-technical teams.

Key Responsibilities :

- Lead the design and implementation of advanced AI applications based on Retrieval-Augmented Generation (RAG) techniques.

- Develop and optimize solutions using Large Language Models (LLMs) (e.g, OpenAI, Anthropic), ensuring high performance and relevance.

- Integrate vector databases (such as Weaviate or PineCone) into AI systems to enhance data retrieval capabilities and improve model accuracy.

- Design and develop Generative AI applications using Langchain, HuggingFace, and similar libraries, focusing on cutting-edge AI techniques.

- Build custom LLM solutions, ensuring efficient model fine-tuning, prompt engineering, and zero-shot/few-shot learning to suit specific business needs.

- Develop and refine prompt engineering strategies to optimize interaction with AI models, ensuring relevant, coherent, and context-aware outputs.

- Architect and implement conversational AI applications that deliver high-quality, human-like interactions.

- Develop robust function calls and APIs that allow seamless integration of conversational agents with other enterprise systems.

- Oversee the preparation, management, and processing of large datasets, ensuring that structured and unstructured data are efficiently handled for training and deployment.

- Implement data pipeline management for AI models, ensuring high data quality and reliability.

- Experience working with data in regulated industries (e.g, healthcare, banking) is desirable, with adherence to relevant regulations such as GDPR or HIPAA.

- Deploy and monitor machine learning models (particularly LLMs) in production environments, ensuring their scalability and reliability.

- Implement model versioning, monitoring, and retraining pipelines to ensure continuous improvement and real-time performance.

- Collaborate with DevOps teams to ensure efficient model deployment on cloud platforms (AWS, GCP, Azure).

- Design and implement scalable AI solutions using cloud infrastructure (AWS, Azure, GCP), focusing on leveraging cloud data services and scalable compute resources.

- Architect solutions based on microservices to ensure system flexibility and maintainability.

- Proficient in deploying applications using serverless architectures or containers (Docker, Kubernetes).

- Work closely with data scientists, machine learning engineers, software engineers, and product managers to define the technical roadmap and ensure successful project delivery.

- Mentor junior engineers and guide the development of AI solutions from inception to deployment.

- Communicate effectively with both technical and non-technical stakeholders, providing clear explanations of complex AI concepts and model behaviors.

- Ensure proper documentation and training resources are available for teams working with AI-driven solutions.

- Minimum of 6-8 years of professional experience as an AI engineer or machine learning engineer, with a focus on Python programming and AI applications.

- Hands-on experience with Retrieval-Augmented Generation (RAG) techniques and knowledge of working with vector databases (e.g, Weaviate, Pinecone).

- At least 1 year of experience with Large Language Models (LLMs) using third-party models such as OpenAI GPT, Anthropic, or similar.

- Proficiency with Generative AI tools and libraries such as Langchain, HuggingFace, and TensorFlow or PyTorch.

- Skilled in prompt engineering and crafting function calls to design complex AI applications like conversational agents.

- Strong knowledge of zero-shot and few-shot learning techniques, with the ability to fine-tune models for specific tasks.

- Proficient in Python and relevant frameworks, with a deep understanding of object-oriented programming, data structures, and algorithms.

- Experience with deploying and managing ML models in production, including version control and continuous integration