We are seeking an AI & Machine Learning Manager with strong hands‑on expertise in AI/ML engineering, Generative AI, data science and enterprise solution delivery.
Responsibilities
Work with business leaders and domain teams to identify, assess and prioritise AI/ML opportunities based on business value, feasibility, risk and expected return.
Translate business problems into clear AI use cases, functional requirements, solution options, delivery plans and measurable success criteria.
Define AI roadmaps and support business cases, proof‑of‑concept assessments and the transition of successful pilots into production solutions.
Lead the design, development, testing and deployment of machine learning, deep learning, NLP, predictive analytics and Generative AI solutions.
Guide the development of LLM‑based applications, Retrieval‑Augmented Generation (RAG), AI agents, conversational systems and intelligent automation solutions.
Requirements
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related discipline
Lead complex AI/ML initiatives, design, develop, evaluate, and operationalize enterprise AI solutions, mentor junior data scientists, and collaborate across engineering and product teams.
Responsibilities
Design, develop, evaluate, and operationalize complex AI/ML solutions
Apply classification, regression, ranking, forecasting, clustering, and anomaly detection to enterprise AI use cases
Develop and evaluate intent classification, semantic matching, and model-routing techniques
Estimate execution latency, token demand, inference costs, and resource requirements
Bachelor’s degree in Machine Learning, Statistics, Data Science, Physics, Computer Science, or a related field, or equivalent education and industry experience
Lead the genomic AI portfolio at Tech@Lilly Bengaluru, driving development of genomic language models, multi-omics integration, and AI for genetic medicines.
Responsibilities
Lead development, fine‑tuning, and benchmarking of DNA/RNA foundation models for sequence‑to‑expression, variant effect, and regulatory genomics prediction
Lead the use of generative and predictive sequence models for AAV promoter design
Direct on‑ and off‑target prediction for genetic medicines across DNA, RNA, protein, and phenotype layers
Partner with genetic medicine scientists to translate model output into design recommendations
Integrate genomic model output with human genetic evidence to sharpen target selection
Requirements
Ph.D. in computational biology, bioinformatics, genomics, computer science, machine learning, or a related quantitative field
Senior MLOps & Cloud Infrastructure Engineer responsible for building, operating, and evolving the cloud infrastructure and engineering platforms behind production AI systems.
Responsibilities
Design, provision, and operate scalable infrastructure on GCP
Manage containerised applications and ML workloads with Kubernetes
Build reproducible infrastructure using Terraform
Design and improve CI/CD pipelines for backend services and data pipelines
Improve service availability, resilience, and incident response
Senior ML Engineer to build customer-facing AI platform and deliver production ML systems
Compensation
For this position, our hiring range for base annual pay is estimated to be CAD $160-200k at the time of this posting
Responsibilities
Take customer problems from idea to working prototype quickly, prove or kill an approach with data, and move what works into production
Train, fine-tune, and evaluate models for matching, classification, and extraction problems
Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS
Own the full ML lifecycle: experiment tracking, model versioning, deployment, monitoring, and retraining
Build evaluation that tells us whether something works: labeled golden sets, offline metrics (precision, recall), LLM-as-judge where it fits, and shadow or A/B testing before rollout
Lead design and delivery of Data Science and AI solutions across global operations, mentor junior talent, and translate insights into measurable business outcomes.
Responsibilities
Partner with business leaders to identify high-value opportunities
Collaborate with domain experts, business analysts, data engineers, software engineers, and integration teams
Lead cross-business-unit and global data science initiatives
Develop and validate machine learning models
Deploy and maintain production-ready models and AI applications
Requirements
Degree in a quantitative discipline such as Computer Science, Engineering, Statistics, or Mathematics
Nice to have: Knowledge of sales, manufacturing operations, supply chain, ERP systems, or finance
Develop and advance machine learning methods that learn from and reason across complex biomedical, clinical, molecular, and scientific data.
Compensation
The annual base salary for this position ranges from $106,000.00 to $176,600.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 15.0% of the base salary and eligibility to participate in our share based long term incentive program.
Responsibilities
Develop frontier AI methods for biomedical research
Design and execute rigorous experiments
Set the evaluation standard at scale
Translate models into scientific evidence
Build shared foundation
Requirements
PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology or related quantitative field
Master's degree with 2+ years of relevant industry or research experience
Nice to have: Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research
Nice to have: Experience with biological, molecular, imaging, clinical, or real-world health data
Nice to have: Experience building foundation models or multimodal learning systems for scientific applications
Lead advanced AI research to accelerate drug discovery and development at Pfizer.
Compensation
The annual base salary for this position ranges from $139,100.00 to $231,900.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 17.5% of the base salary and eligibility to participate in our share based long term incentive program.
Responsibilities
Develop frontier AI methods for biomedical research
Design and execute rigorous experiments
Set evaluation standards at scale
Translate models into scientific evidence
Build shared foundations
Requirements
PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology or related field
Master's degree with 5+ years of relevant industry or research experience
Nice to have: Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research
Nice to have: Experience with biological, molecular, imaging, clinical, or real-world health data
Nice to have: Experience building foundation models or multimodal learning systems for scientific applications
Develop and advance machine learning methods that learn from and reason across complex biomedical, clinical, molecular, and scientific data.
Compensation
The annual base salary for this position ranges from $106,000.00 to $176,600.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 15.0% of the base salary and eligibility to participate in our share based long term incentive program.
Responsibilities
Develop frontier AI methods for biomedical research
Design and execute rigorous experiments
Set the evaluation standard at scale
Translate models into scientific evidence
Build the shared foundation
Requirements
PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Computational Biology or related quantitative field
Master's degree with 2+ years of relevant industry or research experience
Nice to have: Experience applying AI in life sciences, biotechnology, healthcare, pharmaceuticals, or biomedical research
Nice to have: Experience with biological, molecular, imaging, clinical, or real-world health data
Nice to have: Experience building foundation models or multimodal learning systems for scientific applications
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