Lead AI Engineer

Who we're looking for…

Role: Lead AI Engineer

Reports To: Head of AI

Location: India. We are prioritizing candidates in the Delhi area. This role is expected to work in the Indian time zone, IST.

Travel: This role will be expected to make annual travel 3-4 times domestically in India and 1-2 times internationally to the US. Note, Knit US does US All Team, in-person company events twice per year.

A little about us…

Knit is the AI-native consumer research platform helping brands automate and accelerate primary research. With our Researcher-Driven AI, we've condensed the entire quant + qual research process from weeks into days (sometimes hours) for 50+ enterprise brands — including Amazon, T-Mobile, Mars, NASCAR, and more. We're on a mission to scale and democratize world-class research. From survey generation to stakeholder-ready reports, our platform is redefining how insights teams operate — and we need your help to push the limits of what's possible.

Overview

The Lead AI Engineer role involves overseeing the development and deployment of advanced AI models, with a focus on LLMs, MLOps, and data engineering, while ensuring alignment with the overarching vision set by the leadership. The Lead AI Engineer collaborates closely with product managers, designers, and cross-functional teams to deliver scalable, high-impact solutions that enhance market research automation and consumer insight generation.

Responsibilities | What you will own…

Key Performance Indicators (KPIs):

• Model Performance | Achieve high accuracy, explainability, and efficiency in deployed AI models, ensuring their real-world applicability.
• Operational Excellence | Implement scalable and reliable systems with minimal downtime, latency, and resource overhead.
• Scalability & Adaptability | Successfully design systems and applications capable of scaling to handle increasing data volumes and diverse user needs.
• User Impact | Drive measurable improvements in user satisfaction and adoption rates of AI-driven features and applications.

Primary responsibilities of this role:

• AI Model Innovation | Lead the creation, fine-tuning, and deployment of LLMs and ML models to extract actionable insights and drive automation.
• MLOps & Data Engineering | Architect and maintain robust CI/CD pipelines, ensure data quality with scalable ETL workflows, and optimize model deployment lifecycles.
• Scalability & Reliability | Ensure all systems, including AI models and applications, can handle growing data and user demands while maintaining performance and stability.
• Cross-Functional Collaboration | Work closely with product managers, designers, and engineering teams to ensure AI solutions are integrated effectively across platforms.

Required Skills & Experiences

• LLM & ML Expertise | Deep experience in developing, fine-tuning, and deploying LLMs and other advanced ML models for 7+ years.
• MLOps Competence | Expertise in CI/CD pipelines, orchestration tools like Kubeflow or MLflow, and model lifecycle management.
• Data Engineering Proficiency | Strong knowledge of designing scalable ETL workflows and managing data pipelines with tools like Spark, Kafka, and Airflow.
• Cloud & Infrastructure Awareness | Hands-on experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
• Innovation Approach | A forward-thinking mindset to identify trends in AI, MLOps, and full-stack development, ensuring the team stays ahead of the curve.

Benefits

Upon joining the Knit team, you will receive a competitive salary + Equity Options if applicable to role, Healthcare Coverage, a company-issued laptop, contributions to the Employee Provident Fund (EPF), holiday time-off, and more

Our Company Values

We are the Championship Team. This means we:

• Are 1% better every day: We approach situations with a growth mindset and ask, "How can we make the business better?" and "What would it take?"
• Play to win: We set audacious goals and push ourselves to achieve them with a bias towards action (When we see a need, we take initiative, and hold ourselves accountable to seeing it through).
• Keep the main thing the main thing: Identify what has the biggest impact and prioritize to focus on it.