airtel
Website:
airtel.com
Job details:
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
team, and stay technical.
Click on Apply to know more.
