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Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesPython
About the role
Key responsibilities & impact- Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud and on-premises environments
- Build production ML and generative-AI solutions and lightweight applications delivering sub-second insights
- Build end-to-end ML pipelines covering data ingestion, feature engineering, training, hyperparameter optimization, evaluation, registration, and automated promotion
- Build and maintain full-stack AI applications integrating model services with UI components, workflow engines, or business-logic layers
- Establish observability, SLOs, safe deployment practices, and incident runbooks
- Lead offline/online and A/B evaluation, drift detection, and automated retraining
- Architect LLM/RAG systems with prompt management, safety guardrails, and optimized inference
- Enforce data quality, lineage, model cards, and data cards; apply privacy-preserving techniques
- Contribute reusable ML/GenAI components such as feature stores, model registries, and experiment-tracking libraries
- Evangelize best practices that improve engineering velocity across squads
- Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets
- Prototype and benchmark algorithms, advise on scalability and production readiness, and co-own model-performance KPIs
- Translate R&D, Manufacturing, and Commercial domain needs into roadmaps
- Mentor teams and communicate technical trade-offs
- Partner with DevOps, Security, Compliance, and Product teams to deliver enterprise-grade AI solutions
Requirements
What you’ll need- Doctorate degree and 2 years of Machine Learning Engineer experience, OR Master’s degree and 6 years, OR Bachelor’s degree and 8 years, OR Associate’s degree and 10 years, OR high school diploma/GED and 12 years
- Minimum of 2 years of experience directly managing people and/or leadership experience leading teams, projects, programs, or directing allocation of resources
- 3–5 years in AI/ML and enterprise software
- Strong command of machine-learning algorithms, including regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, CNNs, RNNs, transformers, and LLM/RAG techniques
- Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale
- Expert knowledge of vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks such as LangChain, LangGraph, and Semantic Kernel
- Proficiency in Python and Java
- Experience with Docker/Kubernetes containerization
- Experience with AWS, Azure, or GCP
- Experience with modern DevOps/MLOps, including GitHub Actions and Bedrock/SageMaker Pipelines
- Strong business-case skills, including modeling TCO versus NPV
- Exceptional stakeholder management and ability to translate complex technical concepts into concise, outcome-oriented narratives
- Biotechnology or pharma industry experience is preferred
- Published thought-leadership or conference talks on enterprise GenAI adoption preferred
- Master’s degree in Computer Science and/or Data Science preferred
- Familiarity with Agile methodologies and SAFe preferred
- Master’s degree with 10–12+ years of experience in Computer Science, IT, or related field, OR Bachelor’s degree with 12–14+ years of experience in Computer Science, IT, or related field
- Excellent analytical and troubleshooting skills
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation
- Ability to manage multiple priorities successfully
- Strong presentation and public speaking skills
Benefits
Comp & perks- A comprehensive employee benefits package
- Retirement and Savings Plan with generous company contributions
- Group medical, dental and vision coverage
- Life and disability insurance
- Flexible spending accounts
- Discretionary annual bonus program
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models where possible
- Career development opportunities
- Work/life balance plans
- Financial plans with opportunities to save towards retirement or other goals