AI Engineer

The most important requirement is hands-on experience in LLM post-training, particularly Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).

This is a client-facing, hands-on technical role. The candidate should be comfortable discussing AI solutions with CTOs, engineering leaders, and AI research teams, while also building and implementing solutions using Python, APIs, and modern AI technologies.

Key Responsibilities Work directly with CTOs, VPs of Engineering, AI researchers, and technical leaders to understand their AI requirements.Design and implement data pipelines for LLM training, fine-tuning, and evaluation.Build datasets for Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).Understand how to improve LLM performance through post-training techniques.Develop AI-powered applications using LLMs, RAG, AI agents, and synthetic data generation.Build automated pipelines for data generation, annotation, quality checks, and model evaluation.Use Python and APIs to develop prototypes and production-ready solutions.Conduct experiments to evaluate data quality, model performance, and pipeline efficiency.Convert business and research requirements into practical AI engineering solutions.Lead technical discussions with senior client stakeholders.Manage projects from initial discovery through implementation and delivery.Mentor engineers and establish reusable engineering standards. Required Skills and Experience 8+ years of experience delivering complex AI, Machine Learning, or data engineering projects.Strong understanding of Large Language Models (LLMs) and their training processes. Hands-on experience with LLM post-training, especially SFT and Reinforcement Learning. Experience with LLM fine-tuning techniques such as LoRA, PEFT, or similar methods.Understanding of RLHF, RLAIF, DPO, PPO, and reward modeling.Strong Python programming skills, including NumPy and Pandas.Experience building AI applications, automated pipelines, and API integrations.Knowledge of RAG, AI agents, synthetic data generation, and model evaluation.Experience designing data collection, annotation, and quality-control workflows.Strong problem-solving and analytical skills.Excellent communication skills and professional English.Ability to communicate technical concepts clearly to senior executives and clients.Experience managing projects independently, from planning to production delivery. Preferred Qualifications Experience training or fine-tuning LLMs in real-world projects.Experience building AI agents and LLM-powered applications.Experience working with AI research teams or frontier AI companies.Experience with data-centric AI, crowdsourcing, and human-feedback datasets.Experience leading technical teams and mentoring engineers.Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.Experience in consulting or customer-facing AI solution engineering. Important Requirement: LLM Post-Training

Candidates must have practical experience in improving an already pretrained language model through post-training.

The two most important areas are:

1. Supervised Fine-Tuning (SFT)

Training an existing LLM using examples of good questions and answers.Preparing high-quality datasets to teach the model specific tasks, skills, or behaviours.Understanding how to prepare training data and evaluate the results.

2. Reinforcement Learning (RL)

Improving an LLM's responses using feedback, reward signals, or preference-based optimization.Understanding approaches such as RLHF, reward modeling, and PPO.Experience with related preference-optimization methods, such as DPO, is also valuable.

Candidates should be able to explain their actual contribution to SFT or RL projects, including the datasets used, training or optimization methods, experiments performed, and improvements measured.

Ideal Candidate Profile

The ideal candidate is a senior AI engineer who combines practical LLM post-training knowledge, strong Python development skills, AI data engineering, and client-facing consulting experience.

They should be able to understand a client's AI challenge, recommend the right data and post-training strategy, build a working solution, and demonstrate measurable improvements.

Recruitment priority: Candidates with hands-on SFT and Reinforcement Learning experience should be prioritized over candidates who have only built applications using LLM APIs or prompt engineering.