AI Engineer

About the Role

We are building an AI-powered, multi-modal RAN optimisation platform and need a technically sharp junior engineer to help design, train, and deploy language model components at the core of the system. You will work on SLM/LLM selection, fine-tuning, RAG pipeline construction, and production-grade hallucination mitigation.

Key Responsibilities

• Evaluate, benchmark, select, deploy and optimise SLMs and LLMs (e.g., Phi-3, Mistral 7B, Llama 3.x, Qwen 2.5) for telecom-domain tasks including prompt-based optimisation, KPI anomaly explanation, and configuration audit, within on-prem/private cloud environments with GPU acceleration.
• Design and implement RAG pipelines integrating PM/CM/FM data, drive test logs, and vendor documentation as retrieval corpora; manage chunking, embedding, and vector store selection.
• Apply LoRA and QLoRA fine-tuning to adapt foundation models on operator-specific network datasets; manage training runs, hyper-parameter sweeps, and evaluation harnesses.
• Implement and maintain hallucination mitigation strategies: grounded generation, self-consistency checks, retrieval verification, confidence scoring, output guardrails, model drift and prompt failure.
• Contribute to the model governance pipeline: versioning, shadow-mode evaluation, A/B comparison, and promotion criteria for production deployment.
• Collaborate with RAN and data engineering teams to ensure model inputs align with real-world PM counter formats, CM schemas, and FM alarm structures.
• Develop AI agent workflows capable of interacting with telemetry, optimisation engines, RCA workflows, and network automation systems using controlled tool invocation and approval guardrails.

Qualifications

Requirements: 

• 1–3 years of hands-on ML/NLP engineering experience (internships and research projects count).
• Strong Python; practical experience with HuggingFace Transformers, PEFT/LoRA, and at least one vector DB (Chroma, Weaviate, pgvector, or similar).
• Solid understanding of transformer architecture, attention mechanisms, tokenisation, and fine-tuning paradigms (SFT, instruction tuning, RLHF basics).
• Experience building or productionising RAG systems: document ingestion, chunking strategy, embedding model selection, retrieval evaluation (MRR, NDCG, faithfulness).
• Familiarity with hallucination failure modes and at least one mitigation approach in production (citation grounding, chain-of-thought, self-RAG, or ROME/MEMIT-style factual correction).
• Comfortable with experiment tracking (MLflow, W&B) and reproducible training workflows.
• Familiarity with containerized AI workloads using Docker and Kubernetes.
• Understanding of GPU scheduling, model serving frameworks (vLLM, Triton, TGI, Ollama, or similar).
• Awareness of responsible AI practices, model governance, prompt security, and data privacy considerations in enterprise environments.

Good to Have:

• Exposure to structured, semi-structured, and time-series data formats; familiarity with data lakehouse architectures and data pipeline concepts is an added advantage.
• Experience with quantisation (GPTQ, AWQ, bitsandbytes) for edge or on-premises inference.
• Published work, GitHub contributions