What you will be doing You will lead a team of AI & machine learning engineers and managers, driving the design and delivery of production-grade AI solutions - from classical machine learning models to LLM-powered applications - and the pipelines that power them. You'll bring senior technical judgment to architecture and model decisions, partner closely with clients and business stakeholders - including hands-on pre-sales work shaping proposals and solution designs - and define how context engineering, agent harnesses, and fine-tuning practices get embedded into every solution, while reporting into senior AI/technology leadership on strategy and priorities.
- AI & ML Solution Architecture: Leading the design, build, and optimization of production AI systems - classical machine learning models, LLM applications, and agentic systems - ensuring scalability, reliability, and cost-efficient inference.
- Context Engineering: Defining and standardizing context engineering practices - prompt and system design, RAG architectures, vector stores, memory management, and tool/function calling - so models receive the right information at the right time.
- Harness Engineering: Directing the build of robust agent harnesses - orchestration layers, evaluation frameworks, guardrails, and observability - that make LLM systems reliable, safe, and measurable in production.
- Fine-Tuning Pipelines: Leading the design and operation of fine-tuning and model adaptation pipelines - training data curation, supervised fine-tuning, evaluation, and deployment - to specialize models for client use cases.
- Platform Stack: Architecting and deploying solutions on Google Gemini Enterprise and Vertex AI as the primary stack, applying working knowledge of Microsoft AI Foundry and AWS Bedrock where client contexts require.
- Team Leadership: Managing, mentoring, and developing a team of AI & ML engineers; setting technical standards and fostering best practices and knowledge sharing.
- Pre-Sales & Business Development: Supporting pre-sales activities - scoping engagements, building demos and proofs of concept, and presenting solution architectures to prospective clients alongside account teams.
- Machine Learning Modeling: Overseeing the development of classical and modern ML models - predictive modeling, forecasting, recommendation, and deep learning - choosing the right technique for each business problem, LLM or not.
- Contributing to AI Strategy: Partnering with senior leadership to shape GenAI architecture direction, tooling decisions, and platform roadmap within your area.
What we are looking for - The ideal candidate has a substantial Data Science and machine learning background with 8+ years of experience, including at least 2-3 years working on LLM architecture, agentic design, and harness & context engineering.
- Expertise in generative AI/LLM engineering (context engineering, agent harnesses, RAG, and fine-tuning) and in classical machine learning modeling, with proven production deployments.
- Master's degree (or higher) in computer science, engineering, statistics/mathematics, or a related field.
- Hands-on command of core machine learning libraries (scikit-learn, XGBoost, etc.), agentic SDKs (LangGraph/LangChain, Google ADK, Claude Agent SDK), and fine-tuning frameworks (PyTorch, TensorFlow).
- Experience building fine-tuning pipelines end to end: training data curation, supervised fine-tuning, evaluation, and deployment.
- Solid grasp of AI system design: ML model lifecycle (MLOps), agents, tool use, evaluation harnesses, guardrails, and observability.
- Deep experience with Google Gemini Enterprise / Vertex AI; basic working knowledge of Microsoft AI Foundry and AWS Bedrock.
- Experience leading and growing engineering teams, and supporting pre-sales: proposals, demos, and solution scoping with clients.
- Excellent communication skills and comfort collaborating across teams and with stakeholders.
- Strong business acumen with an interest in business-facing work.
- Adaptability and a start-up mentality to thrive in a dynamic environment.
Preferred:- Google Gemini Enterprise ecosystem (Vertex AI, Agent Builder) as the primary stack; basic knowledge of Microsoft AI Foundry and AWS Bedrock
The estimated base compensation for this role starts at $200,000 (NYC location). Individual compensation is determined by skills, qualifications, and experience. In addition, this role is eligible for competitive benefits.
