Principal Machine Learning Engineer

Roles & Responsibilities

• Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem.
• Build production ML/GenAI solutions and lightweight apps delivering sub-second insights.
• Build end-to-end ML pipelines —data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion—using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks.
• Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency.
• Establish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks.
• Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining.
• Architect LLM/RAG with prompt management, safety guardrails, and optimized inference.
• Enforce data quality, lineage, and model/data cards; apply privacy-preserving techniques where needed.
• Contribute reusable ML/GenAI components —feature stores, model registries, experiment-tracking libraries—and evangelize best practices that raise engineering velocity across squads.
• Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
• Prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.
• Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs.

Basic Qualifications

• Doctorate degree and 2 years of Machine Learning Engineer experience OR Master's degree and 6 years of Machine Learning Engineer experience OR Bachelor's degree and 8 years of Machine Learning Engineer experience OR Associate's degree and 10 years of Machine Learning Engineer experience OR High school diploma / GED and 12 years of Machine Learning Engineer experience
• Minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources.
• 3-5 years in AI/ML and enterprise software.
• Strong command of machine-learning algorithms — regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques—with the judgment to choose, tune and operationalize the right method for a given business problem.
• Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
• Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel).
• Proficiency in Python and Java; containerization (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
• Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives.
• Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.

Preferred Qualifications

• Experience in Biotechnology or pharma industry is a big plus
• Published thought-leadership or conference talks on enterprise GenAI adoption.
• Master's degree in Computer Science and or Data Science
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.
• 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
• Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.

Soft Skills

• 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.
• Team-oriented, with a focus on achieving team goals.
• Ability to learn quickly, be organized and detail oriented.
• Strong presentation and public speaking skills.