Applied AI Engineer

About the role

LLM Engineer (GenAI) We are seeking a Deep Learning Architect with 3-4 years of experience to help design and build production-grade GenAI systems. In this role, you will contribute architecture coverage across the team-reviewing system designs, identifying gaps, and guiding technical decisions at the solution level. You will work on end-to-end LLM system design, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent architectures, with a strong focus on production readiness. Strong coding depth is non-negotiable. What You'll Do & The Impact You'll Have Design and contribute to end-to-end LLM system architecture for real-world enterprise use cases (from requirements to production). Pre-train, fine-tune LLMs and domain-specific models using techniques such as CPT, SFT, LoRA, and QLoRA for client-specific use cases. Design and run model evaluation pipelines to benchmark performance, accuracy, and cost across different fine-tuning approaches. Optimise models for latency, throughput, token efficiency, and inference cost in production environments. Implement prompt versioning, rollback strategies, and model monitoring to ensure reliability post-deployment. Collaborate with cross-functional stakeholders (engineering, data, product, client teams) to deliver high-impact solutions on tight timelines.

What you'll bring

What You'll Bring to the Table Must-Have Skills 3-6 years of experience in ML engineering, LLMs, or model development roles. Hands-on experience with Continual pre-training (CPT), supervised fine-tuning (SFT), LoRA, or QLoRA on LLMs. Strong Python programming skills. Ability to write clean, testable, production-ready code. Experience running model evaluation and benchmarking pipelines in a structured way. Solid understanding of transformer architectures and how fine-tuning affects model behaviour. Experience deploying fine-tuned and pre-trained models to cloud environments with attention to cost and latency. Nice-to-Have Skills Experience with AWS-native GenAI building blocks (e.g., Bedrock, OpenSearch, Lambda, ECS/EKS) and secure enterprise deployments. Experience with vector databases/search engines (OpenSearch, Pinecone, Weaviate, Milvus, FAISS) and retrieval optimization. Experience with containerization and orchestration (Docker, Kubernetes). Experience building evaluation/observability pipelines for LLM systems and implementing safety/guardrail patterns. Consulting or client-facing delivery experience.

Benefits and perks

What We Bring to the Table Impact-first culture where your work ships to production and drives measurable business outcomes. Professional growth through mentorship, continuous learning, and exposure to diverse enterprise environments. A high-performing team that values curiosity, integrity, ownership, and engineering excellence. Opportunities for regional and international exposure as projects require.