Lead Machine Learning Engineer(7-11 Years)

Airtel Digital; Gurugram District; Hybrid; INR 2,800,000 - 6,000,000; Full time

Job summary

Airtel Digital seeks a Lead ML Engineer to drive the ML charter for our streaming platform (liveTV, movies, shows) and own the recommendation and search stack end to end.

This hybrid role combines individual contributor work with leading DS/ML engineers. Partner with Product, Engineering and Data teams to translate business goals into crisp ML problems and measurable outcomes.

You will build and evolve systems for content discovery, personalization, and search quality that delight viewers.

Qualifications

• 8+ years of experience building and shipping ML systems at scale.
• Hands-on expertise with Kubernetes, PySpark, scikit-learn, pandas, and PyTorch.
• Strong grounding in classical ML and modern deep learning.
• Experience with ML lifecycle tooling (Airflow, MLflow).
• Exposure to vector search and retrieval augmented ranking.
• Ability to design low latency, high throughput serving systems.

Responsibilities

• Own DS, AI and ML projects end to end, including pipelines and deployment.
• Lead a team of ML engineers and mentor on system design and coding standards.
• Prototype models and ship them to production.
• Develop and evolve recommendation and search systems across content types.
• Collaborate with Data Engineering, Platform, Backend and Product.

Skills

Strong communication

Team leadership

Agile mindset

Tools

Kubernetes

PySpark

scikit-learn

pandas

PyTorch

Airflow

MLflow

Vector search

ANN indexes

Learning to rank

Job description

We are looking for a Lead Machine Learning Engineer to drive the ML charter for our streaming platform that serves our entertainment business serving liveTV, movies, TV shows, and microdramas to millions of viewers. You will own the recommendation and search stack end to end, from data pipelines and feature engineering to model training, evaluation, and low latency online serving.

This is a hybrid role where you will operate as a strong individual contributor while also leading a team of DS and ML engineers.

You will partner closely with Product, Engineering and Data teams to translate ambiguous business goals into crisp ML problem statements and measurable outcomes.

The focus is purely on content discovery, personalization, and search quality that delights the viewer.

What You Will Do

Own DS, AI and ML projects end to end. This includes designing ETL pipelines, feature stores, training workflows, evaluation frameworks, and production deployment while meeting strict SLAs for real time inference.

Lead a team of ML engineers. Set technical direction, review designs and code, grow the craft of the team, and unblock delivery.

Operate as an individual contributor on the hardest problems. Prototype models, run offline and online experiments, and ship them to production yourself when needed.

Strong hands on knowledge of retrieval and ranking models, and bandits, with the ability to reason about when to use each in a recommendation or search stack.

Convert fuzzy business requirements into well scoped ML requirements. Define success metrics, guardrails, and experimentation plans in partnership with Product.

Build and evolve the recommendation and search systems across liveTV, movies, TV shows, and microdramas. This spans candidate generation, ranking, re ranking, cold start handling, and personalization.

Work cross functionally with Data Engineering, Platform, Backend, and Product. Represent the ML team in external forums, design reviews, and leadership updates.

Drive engineering excellence in the team. This includes observability, model monitoring, drift detection, A B testing infrastructure, and post launch analysis.

Mentor engineers on ML system design, coding standards, and production readiness.

What You Bring

8+ years of experience building and shipping ML systems at scale, with a strong track record in production impact rather than only offline results.

Deep, non-negotiable hands on expertise with Kubernetes, PySpark, scikit learn, pandas, and PyTorch.

Strong grounding in classical ML and modern deep learning. Comfort with embeddings, two tower models, sequence models, gradient boosted trees, and learning to rank.

Solid experience with orchestration and ML lifecycle tooling such as Airflow and MLflow.

Exposure to vector search, ANN indexes, retrieval augmented ranking, and neural information retrieval.

Proven ability to design low latency, high throughput serving systems and to reason about tradeoffs between accuracy, latency, and cost.

Excellent written and verbal communication. Comfort presenting to senior stakeholders and translating between business and ML language.

Experience leading small teams of engineers while continuing to write and ship code.

Agile enough to wear multiple caps at same time and leading multiple charters at same time.

Nice to Have

1. 2. Experience with online learning, contextual models, and large scale A B testing platforms.

Familiarity with content understanding signals such as video, audio, and text embeddings for cold start.

3. Publications or open-source contributions in the recommender systems or IR space.

Why This Role

You will work on problems that directly shape what millions of viewers watch next. The surface area is large, the data is rich, and the team is small enough that your decisions will visibly move product metrics. You will get to build a modern ML stack from first principles, lead a talented