Sr Manager, AI Solutions Tech Lead

Role Responsibilities

• Act as the hands-on tech lead for AI product delivery: orchestrate the build across blended teams of 10+ internal engineers and external contractors, while remaining hands-on through pull request reviews and targeted feature development.
• Set, document, and enforce engineering standards, best practices, and development hygiene across the team; influence adherence across contributors beyond direct reporting lines, including contractor and vendor teams.
• Drive technical decision-making: analyze trade-offs, formulate proposals with clear rationale, and present recommendations to technical teams, product owners, and leadership stakeholders.
• Safeguard solution quality end to end: enforce thorough testing (unit, integration, etc.), code review discipline, and release readiness, applying the engineering rigor appropriate to each maturity stage — from POC to MVP to enterprise-grade production.
• Analyze integrations, complex system dependencies, and cross-solution designs; identify technical debt and improvement opportunities and drive their prioritization and resolution.
• Operate fluently across the digital solution stack — data products, AI data products, generative/agentic AI solutions, and full-stack applications — and guide the team on sound implementation choices for each.
• Coordinate and manage delivery through external vendor and contractor teams alongside internal engineering resources, with emphasis on the quality of deliverables and adherence to team standards.
• Collaborate extensively with product owners, core engineering and data science teams, leadership stakeholders, and contractors; communicate technical concepts clearly to technical and non-technical audiences.
• Stay current with emerging technologies and approaches (including agentic AI); evaluate their applicability and foster a collaborative learning environment within the team.

Basic Qualifications

• Bachelor's degree in a relevant discipline (Computer Science, Data Science, Computer Engineering, Information Systems, or a related field) and 7+ years of hands-on experience in AI, data, or software solution engineering, with a track record of building, deploying, and testing complex software systems.
• Demonstrated technical leadership experience as a tech lead or equivalent, orchestrating teams of 10+ members, including blended internal and contractor teams.
• Experience coordinating with and managing external vendor or contractor teams, with emphasis on delivery quality.
• Deep expertise in at least one relevant discipline (e.g., data engineering, data science and ML engineering, AI engineering, backend/full-stack development), with working exposure across the others.
• Solid understanding of the digital product development lifecycle, including the distinction between POC, MVP, and enterprise-grade systems and the engineering practices each stage requires.
• Strong critical thinking, technical judgment, and trade-off analysis; ability to formulate proposals, analyses, and decision rationale.
• Fluency in Python and proficiency in SQL; ability to write clean, efficient, and maintainable code.
• Experience working in a cloud-based analytics ecosystem (e.g., AWS, Snowflake) and familiarity with modern data tooling (e.g., Airflow, dbt) and the standard AI engineering stack.
• Solid grounding in cloud infrastructure and backend engineering, CI/CD, and testing practices; awareness of MLOps/LLMOps.
• Hands-on experience working in agile teams, processes, and practices, and proficiency with agile delivery tooling (e.g., Jira, Confluence).
• Ability to influence and align contributors who are not direct reports.
• Demonstrated willingness and flexibility to learn new technologies and approaches; an exploratory mindset.
• Strong English communication skills (written and verbal).

Preferred Qualifications

• Advanced degree in a relevant discipline.
• Experience building generative and agentic AI solutions (e.g., orchestrating agents on top of existing services and front ends).
• Hands-on experience with MLOps/LLMOps practices and tooling.
• Front-end development experience (the emphasis of the role remains on back-end engineering).
• Experience leading a diversity of digital solution and product types.
• Pharma & Life Science commercial functional knowledge.
• Pharma & Life Science commercial data literacy; awareness of regulatory and legal considerations applicable to commercial solutions.

Non-Standard Work Schedule, Travel or Environment Requirements

• Flexibility in working hours is required to collaborate with international stakeholders across regions (e.g., Latin America, Europe, Asia — including markets such as Mexico, Brazil, Korea, and Japan). Occasional travel may be expected.

Organizational Relationships