Who we’re looking for… 

Role: Sr AI Engineer [MLOps]

Team: AI

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

This Senior AI Engineer [MLOps] role focuses on developing and deploying generative AI models to streamline market research automation, working closely with product managers, designers, and engineers. By leveraging cutting-edge LLM techniques, this position ensures that Knit’s platform can rapidly produce high-quality consumer insights at scale, boosting efficiency and competitive advantage. 

Responsibilities | What you will own… 

Key performance indicators for this team & role:

Key Performance Indicators (KPIs):

• Model Quality | High-fidelity outputs with robust performance, improved accuracy, and relevance in both ML and LLM pipelines.
• Data Infrastructure | Seamless data ingestion, transformation, and accessibility, ensuring scalability and efficiency.
• Operational Excellence | Streamlined deployment pipelines, reduced downtime, and efficient resource utilization.

Primary responsibilities of this role:

• Design & Optimize ML, LLM, and Data Engineering Solutions | Architect end-to-end pipelines, fine-tune models, and integrate data workflows for actionable insights and trustworthy outputs.
• MLOps & Automation | Develop scalable CI/CD pipelines for model deployment, monitoring, and version control. Ensure reproducibility and operational reliability across environments.
• Data Engineering | Design robust ETL pipelines, manage data lakes/warehouses, and ensure high-quality, secure data for training and inference.
• Validation & Compliance | Conduct pilot tests, ensure adherence to regulatory standards, and mitigate bias in models and data workflows.
• Continuous Improvement | Monitor system performance, optimize pipelines, and contribute to transitioning AI capabilities from niche use cases to mainstream adoption.

Required Skills & Experiences

• LLM & ML Expertise | Proven experience in designing, fine-tuning, and deploying advanced models for 3+ years.
• MLOps Mastery | Strong proficiency in model deployment, orchestration tools (e.g., Kubeflow, MLflow), and CI/CD best practices.
• Data Engineering Skills | Hands-on experience in building and optimizing data pipelines, working with tools like Apache Spark, Kafka, or Airflow.
• Cloud Proficiency | Expertise in cloud platforms (AWS, GCP, Azure) and containerized deployments (Docker, Kubernetes).
• Communication & Analysis | Strong ability to explain technical concepts, align AI initiatives with business goals, and collaborate across teams.
• Adaptability | Eagerness to stay ahead of trends in AI, MLOps, and data engineering, continuously refining skills and approaches.

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.