Data Scientist

We are seeking highly skilled AI Engineers with strong expertise in Generative AI and Agentic AI systems to design, build, and deploy next-generation intelligent applications. The role involves developing large-scale AI solutions leveraging LLMs, agentic workflows, multimodal RAG, and real-time AI architectures for enterprise and automotive use cases. You will work on cutting-edge AI initiatives involving Large Language Models, Multimodal RAG, intelligent agents, prompt optimisation, and scalable AI platform development.

The candidate will have responsibilities across the following functions:

Generative AI and Agentic AI Development:

• Design and build advanced Agentic AI systems using frameworks such as LangChain and LangGraph.
• Implement agent orchestration patterns including ReAct, ReWOO, LLM Compiler, and autonomous workflows.
• Develop enterprise-grade AI applications powered by Large Language Models (LLMs).

Multimodal RAG Solutions:

• Build Retrieval-Augmented Generation (RAG) solutions capable of processing text, images, audio, and video.
• Design efficient chunking, indexing, clustering, and retrieval strategies for large-scale datasets.
• Optimise vector search and knowledge retrieval pipelines.

Prompt Engineering and LLM Optimisation:

• Develop advanced prompting techniques, including: Chain-of-Thought (CoT), Self-Reflection Prompting, LLM-as-a-Judge
• Optimise prompt performance using modern tools such as DSPy, TextGrad, AdaFlow, and LLMLingua.
• Implement context-window optimisation and prompt-compression techniques.

AI Platform and API Development:

• Develop scalable AI services and APIs using FastAPI.
• Build AI model-serving layers and inference pipelines.
• Integrate AI capabilities with enterprise applications and backend systems.

Data Engineering and AI Pipelines:

• Design and implement end-to-end data ingestion and processing pipelines.
• Develop workflows for document processing, retrieval, and AI model inference.
• Handle large-scale structured and unstructured datasets.

Streaming and Real-Time AI Applications:

• Develop low-latency AI systems for real-time audio and video processing.
• Build streaming architectures supporting real-time inference and decision-making.

NL2SQL and Intelligent Data Access:

• Develop Natural Language to SQL (NL2SQL) solutions.
• Build conversational interfaces for enterprise databases.
• Optimise AI-generated queries for performance, accuracy, and reliability.

Cloud and Deployment:

• Deploy containerised AI applications using Docker and Kubernetes.
• Implement scalable deployment, monitoring, and observability strategies.
• Collaborate with DevOps teams to ensure production readiness and reliability.

The core requirements for the job include the following:

Generative AI and LLMs:

• Strong hands-on experience with Large Language Models (LLMs).
• Expertise in LangChain, LangGraph, and Agentic AI frameworks.
• Proven experience building enterprise-grade Generative AI applications.

AI/ML Frameworks:

• Strong experience with: PyTorch, TensorFlow, and Scikit-learn.

Programming and API Development:

• Strong proficiency in Python.
• Experience developing scalable APIs and AI services using FastAPI.

RAG and Vector Technologies:

• Hands-on experience with RAG architectures and multimodal retrieval.
• Strong understanding of vector databases, embeddings, indexing, and semantic search.

Cloud and Infrastructure:

• Experience with Docker and Kubernetes.
• Understanding of scalable AI deployment and production environments.

Preferred Skills:

• Experience with real-time audio/video AI applications.
• Knowledge of NL2SQL and conversational AI systems.
• Experience with prompt optimisation frameworks such as DSPy, TextGrad, AdaFlow, or LLMLingua.
• Experience working with enterprise or automotive AI use cases.