Provide technical leadership for the end‑to‑end development of AI/ML systems, from data ingestion and feature engineering to model deployment and monitoring.
Define the architectural direction for machine learning platforms, ensuring scalability, security, performance, and alignment with organizational standards.
Lead the evaluation, selection, and integration of AI technologies, frameworks, and tools, including GenAI solutions.
Collaborate with product, and domain teams to translate business requirements into well‑designed ML architectures and solution roadmaps.
Oversee ML model lifecycle management, including experimentation, versioning, CI/CD integration, and continuous improvement.
Mentor and guide data scientists, ML engineers, and analysts, fostering technical excellence and knowledge sharing.
Conduct reviews of ML pipelines, code, and solution designs to ensure quality, maintainability, and adherence to best practices.
Drive the implementation of MLOps practices such as automated model training, deployment, monitoring, and retraining workflows.
Partner with cross-functional teams to troubleshoot complex ML and data pipeline issues and develop long‑term solutions.
Stay current with emerging trends in AI/ML and GenAI, advising on opportunities to adopt new techniques and accelerate innovation.
Ensure data quality, governance, compliance, and responsible AI principles are integrated into all AI/ML initiatives.
Promote continuous improvement and help establish best practices in AI architecture, experimentation, model evaluation, and documentation.
What you will bring:
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field; Masters degree in Data Science is preferable
9–12 years of experience in AI/ML development, advanced analytics, or data‑driven software engineering.
Deep expertise in machine learning algorithms, statistical modeling, neural networks, NLP, or computer vision.
Strong proficiency in Python and experience with AI/ML libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Keras etc.
Hands‑on experience designing and deploying ML models into production environments at scale.
Strong understanding of GenAI techniques, including LLMs, embeddings, vector databases, and prompt‑engineering concepts.
Proficiency with SQL and experience working with large, complex datasets.
Experience with cloud-based AI/ML platforms such as AWS SageMaker, Azure ML, or Google Vertex AI.
Familiarity with MLOps tools and practices including CI/CD, model registries, feature stores, and monitoring frameworks.
Strong understanding of distributed computing, microservices, APIs, and data engineering concepts.
Knowledge of ML security, governance, and responsible AI guidelines is a plus.
Soft Skills:
Proven leadership abilities with experience mentoring and guiding technical teams.
Excellent communication and storytelling skills for conveying complex AI concepts to technical and non‑technical audiences.
Strong stakeholder management and the ability to collaborate effectively across functions.
Strategic thinker with the ability to define long‑term AI roadmaps while driving short‑term execution.
Strong problem‑solving and decision‑making capabilities, especially in ambiguous or fast‑changing environments.
Highly organized with the ability to manage multiple projects and deliverables simultaneously.
Proactive, innovative mindset with a strong focus on quality, reliability, ethics, and continuous improvement.
We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.
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