About the Role:
We are looking for a visionary AI/ML Lead to own our end-to-end artificial intelligence strategy and execution. This role will bridge cutting-edge research and real-world product impact by leading cross-functional teams of engineers, data scientists, and product stakeholders to build AI systems that are intelligent, reliable, scalable, and responsible.
Key Responsibilities
- Define and drive the AI roadmap across generative AI, agentic workflows, multimodal systems, and LLM-powered enterprise products.
- Lead, mentor, and grow a high-performing team of AI/ML engineers, prompt engineers, and applied scientists.
- Design and oversee the full AI lifecycle including problem framing, data strategy, feature engineering, model development, evaluation, deployment, and production monitoring.
- Architect multi-agent systems and AI pipelines using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent orchestration tools.
- Collaborate with product, engineering, and business teams to embed AI capabilities into core user workflows, internal platforms, and client-facing solutions.
- Champion responsible AI by defining governance frameworks, evaluation standards, model guardrails, bias mitigation practices, and secure deployment standards.
- Drive adoption of MLOps and LLMOps practices including CI/CD, observability, model evaluation, prompt version control, and continuous improvement.
- Stay ahead of advancements in foundation models, applied AI frameworks, and enterprise AI architecture, converting innovation into practical business offerings.
- Partner with leadership teams and clients to identify high-value AI opportunities and translate them into scalable solutions and delivery roadmaps.
Required Skills and Experience
- 9+ years of experience in Data Science, Machine Learning, AI Engineering, or advanced analytics roles.
- Strong proficiency in Python for machine learning, model development, data engineering, and AI application development.
- Proven hands-on experience in ML model development, evaluation, deployment, and production support.
- Strong experience with Apache Kafka and streaming data processing.
- Deep understanding of feature engineering, model evaluation, experimentation, and production ML workflows.
- Strong working knowledge of SQL and large-scale data processing environments.
- Experience with AWS services and cloud-native ML tooling, including deployment and monitoring patterns.
- Hands-on experience with Snowflake and Snowpark for scalable enterprise data workflows.
- Experience building CI/CD pipelines and deployment automation for AI/ML systems.
- Hands-on experience with Terraform and infrastructure-as-code practices for cloud infrastructure management.
Preferred Qualifications
- Experience with Generative AI, LLM applications, RAG architectures, agentic AI systems, and prompt engineering.
- Exposure to multimodal AI solutions, conversational AI, and enterprise workflow automation.
- Experience in consulting-led delivery environments and working with global enterprise clients.
- Strong people leadership, stakeholder management, and solution consulting skills.