AI Engineer

We are looking for an AI Engineer with strong AI/ML and GenAI systems expertise to build and produce next-generation AI systems. This role is for someone who can go beyond using frameworks someone who understands how models and AI systems work internally, can design the architecture around them, evaluate them rigorously, and take a prototype all the way to a scalable, production-grade system.

The candidate will have responsibilities across the following functions: AI/ML and Multimodal AI:

• Strong first-principles understanding of Machine Learning, Deep Learning and Computer Vision.
• Deep understanding of LLMs and Vision-Language Models (VLMs) tokenization/encoding, vision and language representations, multimodal alignment and how semantic information is fused.
• SFT for LLMs/VLMs, PEFT/LoRA and fine-tuning strategies.
• Model optimisation: quantisation (AWQ, INT4, HQQ), distillation, batching and inference optimisation.
• vLLM and high-performance model serving.
• Reward modelling, custom/verifiable rewards, DPO, RLHF and GRPO.
• Hands-on experience with PyTorch, Hugging Face Transformers and TRL.

RAG, Retrieval and AI Agents:

• Build advanced RAG / GraphRAG / VisionRAG systems.
• Retrieval and ranking: BM25, semantic retrieval, hybrid search, vector databases.
• Understand and implement RRF, MRR, Recall, Precision, F1, RAGAS and other evaluation approaches.
• Query optimisation using HyDE, query expansion and rewriting.
• Agentic systems using LangGraph, LangChain, CrewAI, Agno or equivalent.
• Understanding of MCP, its communication/transport mechanisms, tool calling and context engineering.
• Structured output generation, schema validation and reliable tool execution.

Evaluation and ML Engineering:

• Build evaluation harnesses, automated testing and benchmarking frameworks.
• Design golden datasets, taxonomies and evaluation datasets.
• Perform model benchmarking, error analysis and root-cause analysis of model failures.
• Design data preprocessing, transformation and ML pipelines.
• Understand model quality vs. latency vs. memory vs. cost trade-offs.
• Experience with distributed ML systems using Ray or equivalent.

Production AI Systems and Architecture:

• Design end-to-end AI/ML system architectures.
• Taking rapid prototypes to robust production systems.
• Build Python/FastAPI microservices and REST APIs.
• Docker/containerization, webhooks and SSE.
• CI/CD, Git/GitHub and production engineering practices.
• Design scalable pipelines involving queues, asynchronous processing and distributed workloads.