Mission:
As a Data Scientist specializing in LLM you will be responsible for developing, implementing and optimizing advanced algorithms, models and capabilities that help our teams (e.g. Field Operations) automate their workloads, e.g. in the context of decision support, mission planning and situational awareness. You will work on a variety of projects that involve understanding, processing and generating human language to solve complex problems and create innovative solutions. The ideal candidate will have a strong background in LLMs, machine learning and data science, with a proven track record of successful projects in these domains.
What will be your responsibilities:
Profile and requirements:
Strong programming skills in Python, with experience using NLP and LLM libraries such as spaCy, Hugging Face (Transformers, Datasets, PEFT, TRL) and the major model families (e.g. GPT, Claude, Gemini, Llama, Mistral, Qwen, Gemma) via both API and open weights.
Proficiency in deep learning frameworks, primarily PyTorch (plus Keras/TensorFlow as needed), and familiarity with inference optimisation (quantisation, TensorRT-LLM).
Experience with data preprocessing, curation and tokenisation for LLM workloads, including building and cleaning datasets for fine-tuning and retrieval (chunking, embeddings, deduplication, synthetic data generation).
Solid understanding of transformer architectures and attention, with working knowledge of fine-tuning and alignment techniques (full fine-tuning, LoRA/QLoRA, instruction tuning, RLHF/DPO). Exposure to RNNs and CNNs is a plus rather than a core requirement.
Experience training and fine-tuning LLMs and building RAG and agentic systems, including orchestration frameworks (LangChain, LlamaIndex, LangGraph), vector databases (e.g. Qdrant, Weaviate, pgvector) and tool/function calling.
Experience with experimentation and tracking tooling: Jupyter notebooks plus experiment and prompt tracking (MLflow, Weights & Biases) and LLM evaluation (e.g. Ragas, LangSmith/Langfuse, custom eval harnesses).
Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their AI services, with a focus on Google Cloud (Vertex AI, model garden, managed endpoints).
Experience deploying self-hosted and open-weight LLMs in production, using serving frameworks such as vLLM, TGI, Ollama or llama.cpp, with awareness of GPU sizing, quantisation formats (GGUF, AWQ, GPTQ) and on-prem or airgapped constraints.
Working knowledge of MLOps/LLMOps and DevOps practices: Git, CI/CD, containerisation (Docker, Kubernetes), plus telemetry, monitoring and observability for model and inference performance.
What we have to offer you:
Pre-Employment Requirements
Employment offers may be contingent upon successful completion of a background check and reference verification, in accordance with applicable laws and company policy.
Equal Employment Opportunity
TEKEVER is an Equal Opportunity Employer. We consider all qualified applicants without regard to race, color, religion, sex (including pregnancy, sexual orientation, gender identity, and gender expression), national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by federal, state, or local law.
Do you want to know more about us ?
Visit our LinkedIn page at https://www.linkedin.com/company/tekever/
