About this role
Shift time: US or Europe time zone
Position Overview:
This senior, client-facing and hands-on role acts as the primary technical counterpart to CTOs, VPs of
Engineering, and research/engineering leaders at AI clients. The role diagnoses client needs, builds on a
solid understanding of how LLMs are trained, fine-tuned, and evaluated, and proactively proposes data
strategies with options and rationale. The person in this role personally designs and builds data solutions,
including data-labeling components, quality controls, user interfaces, and AI-driven systems such as agentic
workflows, RAG, and synthetic data generation. These are integrated into automated, multi-stage pipelines
that produce data for AI training and evaluation. The role also provides technical leadership across multiple
client engagements, establishes reusable engineering standards and best practices, and mentors Solution
Engineers and Technical Consultants. It carries end-to-end ownership of delivery (timelines, quality,
scalability) and the commercial outcome (Gross Margin and Contribution Margin), and drives expansion
opportunities. Strong executive communication, ownership mindset, and hands-on engineering skills are
expected.
Key Responsibilities:
- Act as the primary technical counterpart to CTOs, VPs of Engineering, and research/engineering
leadership
- Lead executive conversations using a structured, answer-first (BLUF) approach
- Manage escalations and expectations with composure and integrity
- Build long-term trusted relationships by recommending evidence-based solutions
- Ask excellent questions to uncover the team real need (the 'question behind the question')
- Understand the team model, the metrics they want to move, and how they intend to train or evaluate it
- Draw on a solid understanding of how LLMs are trained and fine-tuned to hold credible conversations with
technical leaders and understand their data strategy
- Proactively propose the data that will solve the team problem, with options and rationale
- Design and build data solutions hands-on, including configuring data-labeling components and quality
controls
- Develop user interfaces and AI-driven solutions (e.g., agentic systems, RAG, synthetic data generation)
- Integrate solutions into automated, multi-stage pipelines that produce data for AI training and evaluation
- Architect and reason about complex, multi-stage solutions end to end
- Run experiments to prove the pipeline delivers data of the required quality and speed
- Iterate from MVP toward production
- Provide technical leadership across multiple client engagements
- Establish reusable engineering standards and best practices
- Mentor Solution Engineers and Technical Consultants to raise the overall technical bar of the
organization
- Own delivery end to end, including timelines, quality, and scalability
- Own the commercial outcome (Gross Margin and Contribution Margin), naming the levers, trade-offs, and
next checkpoints
- Identify and drive expansion opportunities
Required Skills:
- Executive communication: confidently leading conversations with CTOs, VPs, and senior technical
stakeholders - clear, concise, structured, persuasive, and calm under pressure
- Professional English (C1+)
- Strong understanding of modern LLM development: training, fine-tuning, and evaluation
- Knowledge of SFT, RLHF/RLAIF, DPO/PPO, reward modeling, and LoRA/PEFT
- Ability to translate client goals into effective AI data solutions
- Hands-on solution engineering: designing complex AI solutions and building multi-stage pipelines
- Experience developing AI-driven systems (agentic workflows, RAG, synthetic data generation)
- Python (NumPy, Pandas)
- API integration
- Independently prototyping LLM-powered solutions
- Solid software engineering foundations: version control, testing, MVP thinking, iterative development
- Exceptional discovery and problem framing, working through ambiguity
- Translating business or research goals into clear AI data and evaluation strategies
- 8+ years delivering complex AI, ML, or data projects end to end
- Experience influencing technical direction beyond individual projects
- Ownership mindset: making thoughtful trade-offs between time, cost, and quality, and owning technical
and commercial success
Note: He specifically wants people with post-training experience, including:
SFT (Supervised Fine-Tuning)
Reinforcement Learning
Preferred (Bonus) Skills:
- Experience building production software
- Experience establishing engineering standards, mentoring engineers, or leading cross-functional
technical initiatives
- Experience in top-tier strategy consulting (McKinsey, Bain, BCG) and/or as an applied ML / LLM engineer
- Hands-on experience training or fine-tuning LLMs and/or building agentic systems
- Advanced degree (MSc/PhD) in AI, CS, or a related field
- Experience in crowdsourcing and/or data-centric AI
- Experience with fast-paced, multi-project delivery for frontier AI clients