About this role
<div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Job Description</b></H2> </div><div><p><strong>About the Role</strong></p> <p>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.</p> <p><strong>Key Responsibilities</strong></p> <ul> <li>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.</li> <li>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.</li> <li>Apply LoRA and QLoRA fine-tuning to adapt foundation models on operator-specific network datasets; manage training runs, hyper-parameter sweeps, and evaluation harnesses.</li> <li>Implement and maintain hallucination mitigation strategies: grounded generation, self-consistency checks, retrieval verification, confidence scoring, output guardrails, model drift and prompt failure.</li> <li>Contribute to the model governance pipeline: versioning, shadow-mode evaluation, A/B comparison, and promotion criteria for production deployment.</li> <li>Collaborate with RAN and data engineering teams to ensure model inputs align with real-world PM counter formats, CM schemas, and FM alarm structures.</li> <li>Develop AI agent workflows capable of interacting with telemetry, optimisation engines, RCA workflows, and network automation systems using controlled tool invocation and approval guardrails.</li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Qualifications</b></H2> </div><div><p><strong>Requirements: </strong></p> <ul> <li>1–3 years of hands-on ML/NLP engineering experience (internships and research projects count).</li> <li>Strong Python; practical experience with HuggingFace Transformers, PEFT/LoRA, and at least one vector DB (Chroma, Weaviate, pgvector, or similar).</li> <li>Solid understanding of transformer architecture, attention mechanisms, tokenisation, and fine-tuning paradigms (SFT, instruction tuning, RLHF basics).</li> <li>Experience building or productionising RAG systems: document ingestion, chunking strategy, embedding model selection, retrieval evaluation (MRR, NDCG, faithfulness).</li> <li>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).</li> <li>Comfortable with experiment tracking (MLflow, W&B) and reproducible training workflows.</li> <li>Familiarity with containerized AI workloads using Docker and Kubernetes.</li> <li>Understanding of GPU scheduling, model serving frameworks (vLLM, Triton, TGI, Ollama, or similar).</li> <li>Awareness of responsible AI practices, model governance, prompt security, and data privacy considerations in enterprise environments.</li> </ul> <p><strong>Good to Have:</strong></p> <ul> <li>Exposure to structured, semi-structured, and time-series data formats; familiarity with data lakehouse architectures and data pipeline concepts is an added advantage.</li> <li>Experience with quantisation (GPTQ, AWQ, bitsandbytes) for edge or on-premises inference.</li> <li>Published work, GitHub contributions</li> </ul> <p><strong>We warmly welcome fresh graduate majoring in AI skillsets to apply.</strong></p></div></div></div>