AI Engineering & Data Science Lead

Lonza is hiring an AI Engineering & Data Science Lead in Hyderabad, India. Level rates it ; you can apply on Lonza.

Level's read on this job

Level rates this AI engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
Tools and skills the posting cites include MLOps, AI evaluation and AI safety.
Level classifies it as a lead role.
Level lists 565 other open roles at Lonza.
The role was posted in the last few days.

AI in this role

ml-opsai-evaluationai-safety

Role Purpose

An AI Engineering & Data Science lead is for an AI Engineering and Data Science capability within the Lonza AI Factory (LAIF), defining the engineering standards, reusable capabilities, and technical approaches required to build and scale AI solutions across Lonza.

This role is responsible for shaping the AI intelligence layer, driving AI engineering excellence, and enabling the successful delivery of AI products through cross-functional Fusion Teams.

The position ensures AI solutions are scalable, secure, compliant, and designed for enterprise-wide reuse and adoption.

Key Responsibilities

AI Engineering Strategy, Architecture & Reusable Capabilities

Define the AI engineering and data science strategy, architecture principles, and technical roadmap for the Lonza AI Factory.
Shape and evolve the AI intelligence layer, including reusable services, contextual frameworks, orchestration capabilities, agentic patterns, and shared AI services.
Establish engineering standards and reference architectures covering AI/ML development, evaluation, deployment, monitoring, and lifecycle management.
Own and continuously evolve the approved catalogue of AI models, algorithms, frameworks, tools, and reusable components.
Partner with DDAI, IT, and technology teams to define technical requirements and priorities for AI platform services.
Drive build-versus-buy-versus-partner decisions for AI technologies and continuously evaluate emerging technologies and market trends.

AI Engineering & Data Science Delivery

Lead AI engineering and data science delivery for strategic AI products and business-critical use cases through cross-functional Fusion Teams.
Provide hands-on technical leadership for complex AI solutions, including Generative AI, Agentic AI, Machine Learning, and Advanced Analytics.
Drive the industrialization of AI solutions by transitioning initiatives from proof-of-concept and experimentation into enterprise-scale production deployments.
Establish robust evaluation, testing, validation, performance measurement, and continuous improvement frameworks for AI models and agents.
Promote the reuse of common AI capabilities, engineering patterns, and shared services to maximize efficiency and scalability across products.
Support AI Product Leads and Business Product Owners in translating business challenges into technically viable and impactful AI solutions.

AI Lifecycle, Operations & Responsible Engineering

Define AI and Machine Learning engineering lifecycle standards in partnership with IT and DDAI, including deployment, observability, monitoring, versioning, model governance, and operational resilience.
Embed security, privacy, responsible AI principles, and regulatory requirements into engineering frameworks and development practices.
Establish technical controls for AI evaluation, traceability, explainability, human oversight, and evidence generation, particularly for regulated and GxP-relevant environments.
Collaborate with DDAI and IT teams on AI FinOps, observability, and consumption monitoring to ensure scalable, sustainable, and cost-effective AI operations.

Team Leadership & Capability Building

Support, lead, and scale a high-performing AI Engineering and Data Science organization across multiple locations.
Foster a strong technical community across LAIF, DDAI, and business domains, encouraging knowledge sharing, engineering excellence, and reuse of best practices.
Coach, mentor, and develop AI Engineers and Data Scientists while establishing clear capability frameworks and technical career pathways.
Develop strategic relationships with external technology partners, research institutions, and specialist providers to accelerate Lonza’s AI capabilities and innovation agenda.

Key Requirements

Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
Extensive experience leading AI Engineering, Machine Learning, Data Science, or Advanced Analytics teams in a large-scale enterprise environment.
Deep expertise in Generative AI, Agentic AI, Machine Learning, MLOps, AI Platforms, and AI solution architecture.
Proven track record of delivering enterprise AI solutions from concept through production deployment and operationalization.
Strong understanding of AI governance, responsible AI, security, privacy, and compliance requirements within regulated industries.
Experience building and scaling global technical teams and driving organizational capability development.
Excellent stakeholder management and communication skills with the ability to influence senior leadership and cross-functional teams.
Experience working within life sciences, healthcare, biopharmaceutical, or similarly regulated industries is highly desirable.

How we rate this

AI Engineering & Data Science Lead at Lonza rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

AI Engineering & Data Science Lead is an open job at Lonza.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in IN - Hyderabad.
The job was posted on November 3, 2026.
Seniority: lead.
Applications are handled by Lonza. Apply on Lonza

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsAI EvaluationAI Safety

Also common in AI engineer jobs at this level

Python LLM AWS SQL AI agents Azure

Pay

Typical AI engineer job: $183k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
How do you decide that one model's output is better than another's for a given task?
How do you think about the risk of an AI system in this kind of role failing silently?
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: ML Ops, AI Evaluation, and AI Safety. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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