Senior AI Engineer
Summary
Build and deploy small/large language models to optimize telecom RAN networks, focusing on RAG pipelines, fine-tuning, and hallucination mitigation in a private-cloud environment.
Job Description 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.
- 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.
- 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