Engineering Manager- AI & ML

Position: Engineering Manager- AI & ML )
As an Engineering Manager, AI & ML (Data Collection), you will play a critical role in building
and scaling the company’s Unified AI/ML Data Collection Platform, enabling
standardized, reliable, and scalable machine learning capabilities across the organization.

This role will focus on transforming existing AI/ML and LLM-driven data systems into a
cohesive platform that supports data pipelines, model lifecycle management, evaluation
frameworks, and production deployment.

This position requires deep technical expertise in machine learning systems, ML platform
architecture, and MLOps, along with a strong ability to lead and mentor engineering teams.

You will work closely with individual contributors and cross-functional partners to ensure
that ML platform capabilities align with broader business objectives and AI/ML strategies.
You will be deeply involved in the design, development, and operationalization of platform
components, including data ingestion, feature management, model training and
evaluation, and scalable inference systems. You will provide strong technical leadership,
solve complex system-level challenges, and ensure delivery of high-quality, reliable, and
scalable ML solutions.

Your leadership will ensure that AI/ML systems are production-ready, observable, and
maintainable, with a strong emphasis on performance, cost-efficiency, and governance.
You will leverage your expertise in areas such as large language models (LLMs), retrieval
augmented generation (RAG), ML Operations (MLOps), distributed systems, and cloud
native architectures.
You will oversee the end-to-end lifecycle of ML systems—from development and
experimentation to deployment and monitoring—while ensuring alignment with global
engineering standards and business priorities.

You will be responsible for mentoring engineers, driving technical excellence, and fostering
a culture of collaboration and innovation. Your ability to partner across teams, influence
technical direction, and build high-performing teams will be critical to success in this role.

You will lead a multidisciplinary team of ML engineers responsible for building and
maintaining the Unified AI/ML Data Collection Platform. The team focuses on developing
scalable systems that support data pipelines, model lifecycle management, LLM-based
workflows, and evaluation frameworks, enabling downstream teams to build and deploy AI
driven data collection solutions.

Primary

Job Responsibilities:

• AI-Powered Data Collection Systems:
Lead the design and development of
scalable AI-driven data collection and enrichment workflows across structured and
unstructured data sources.

• LLM & Generative AI Workflows:
Drive the implementation of LLM-based
capabilities including extraction pipelines, RAG systems, prompt orchestration,
summarization, classification, and automated validation workflows.

• Data Pipeline Engineering:
Oversee high-scale ingestion, transformation, and
orchestration systems that support real-time and batch data collection processes.

• Data Quality & Evaluation:
Establish frameworks for evaluating extraction quality,
model performance, hallucination risks, consistency, and overall data reliability.

• Technical Leadership:
Provide hands-on leadership in architecture, system design,
operational scalability, and engineering best practices for AI-enabled data systems.

• Team Leadership & Mentorship:
Build and grow a high-performing engineering
team while fostering a culture of ownership, collaboration, and continuous
improvement.

•

Cross-functional Collaboration:

Partner closely with product management, data
engineering, research, and business stakeholders to align technical investments
with organizational goals.

• Platform Reliability & Governance:
Ensure systems meet standards for scalability,
observability, security, compliance, and auditability.

• Innovation & Continuous Improvement:
Evaluate emerging AI/ML technologies,
frameworks, and tooling to improve automation capabilities and developer
productivity.

• Operational Excellence:
Drive engineering best practices, Agile delivery processes,
and operational maturity across the team.

• Hiring & Talent Development:
Support recruiting, onboarding, and retention…