Job Description

A hands-on Senior AI/ML Engineer who combines deep technical expertise with strong business understanding. This is not a high-level/figurehead Architect role—the individual is expected to work closely with the client's technology partner, translate business requirements into practical AI solutions, and personally design, develop, integrate, and deploy AI assistants.Key Responsibilities

Partner daily with the GCO Technology Partner (forward-deployed model): joint working sessions, direct business/stakeholder calls, rapid iteration on evolving needs with minimal formal requirements.
Design and build LLM-powered assistants (custom GPTs) on the client's approved LLM estate and existing component library.
Build RAG workflows end-to-end: large-scale document ingestion (thousands of documents), chunking/embedding strategy, vector store and index creation, retrieval integration into assistants.
Integrate ML models with assistants: connect, adapt, or lightly train forecasting and predictive models (sales forecasting, commercial analytics) and surface them through conversational experiences.
Guide and review the work of the pod's second engineer; set technical direction and quality bar; sequence the backlog with the technology partner.
Identify when a solution outgrows tactical delivery and should be escalated into the client's product/agentic pipeline; frame the input for that business case.
Operate in agile ceremonies with iterative, hypothesis-driven (hit-and-trial) solutioning and pragmatic, lightweight documentation.

Must-Have Skills

LLM application engineering: prompt design, tool/function calling, assistant/custom-GPT development, evaluation and guardrails.
RAG at production quality: embeddings, vector databases/indexes, retrieval tuning, context management for large document sets.
Machine learning: supervised learning, time-series forecasting and predictive modelling; model training/tuning and evaluation; Python ML stack (pandas, scikit-learn; plus one of statsmodels/Prophet/XGBoost or equivalent).
Strong Python engineering; API design and systems integration; Git-based collaborative development.
Consultative presence: can sit in front of senior business stakeholders, absorb ambiguous asks, and translate them into shippable increments — in a US Central time-zone working pattern.

Good-to-Have

Pharma / life-sciences commercial domain exposure (sales forecasting, field/HCP engagement analytics); familiarity with data-privacy and compliance expectations in regulated environments.
Exposure to enterprise orchestration/automation platforms (e.g., UiPath) and AWS-based ML tooling; MLOps fundamentals.
Experience in forward-deployed / client-embedded engineering models.