Data Scientist

Vayuz Technologies

3 - 5 years

14 - 20 Lacs

Bengaluru

Posted:1 month ago| Platform:

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Skills Required

GCP Vertex Ai Python Cloud Run Multi-Modal Retrieval Models TF Serving

Work Mode

Work from Office

Job Type

Full Time

Job Description

Strong in
Python
with libraries such as polars, pandas, numpy, scikit-learn, matplotlib, tensorflow, torch, transformers • Must have: Deep understanding of
modern recommendation systems
including
two-tower
,
multi-tower
, and
cross-encoder
architectures • Must have: Hands-on experience with
deep learning for recommender systems
using
TensorFlow
,
Keras
, or
PyTorch
• Must have: Experience generating and using
text and image embeddings
(e.g.,
CLIP
,
ViT
,
BERT
,
Sentence Transformers
) for content-based recommendations • Must have: Experience with
semantic similarity search
and
vector retrieval
for matching user-item representations • Must have: Proficiency in building
embedding-based retrieval models
,
ANN search
, and
re-ranking strategies
• Must have: Strong understanding of
user modeling
,
item representations
,
temporal/contextual personalization
• Must have: Experience with
Vertex AI
for training, tuning, deployment, and pipeline orchestration • Must have: Experience designing and deploying
machine learning pipelines on Kubernetes
(e.g., using
Kubeflow Pipelines
,
Kubeflow on GKE
, or
custom Kubernetes orchestration
) • Should have experience with
Vertex AI Matching Engine
or deploying
Qdrant
,
F AISS
,
ScaNN
, on GCP for large-scale retrieval • Should have experience working with
Dataproc (Spark/PySpark)
for feature extraction, large-scale data prep, and batch scoring • Should have a strong grasp of
cold-start problem solving
using metadata and multi-modal embeddings • Good to have: Familiarity with
Multi-Modal Retrieval Models
combining text, image, and tabular features • Good to have: Experience building
ranking models
(e.g.,
XGBoost
,
LightGBM
,
DLRM
) for candidate re-ranking • Must have: Knowledge of
recommender metrics
(Recall@K, nDCG, HitRate, MAP) and
offline evaluation frameworks
• Must have: Experience running
A/B tests
and interpreting results for model impact • Should be familiar with
real-time inference
using
Vertex AI
,
Cloud Run
, or
TF Serving
• Should understand
feature store concepts
,
embedding versioning
, and
serving pipelines
• Good to have: Experience with
streaming ingestion
(Pub/Sub, Dataflow) for updating models or embeddings in near real-time • Good to have: Exposure to
LLM-powered ranking or personalization
, or hybrid recommender setups • Must follow
MLOps practices
version control, CI/CD, monitoring, and infrastructure automation
GCP Tools Experience:

ML & AI
: Vertex AI, Vertex Pipelines, Vertex AI Matching Engine, Kubeflow on GKE, AI Platform
Embedding & Retrieval
: Matching Engine, FAISS, ScaNN, Qdrant, GKE-hosted vector DBs (Milvus)
Storage
: BigQuery, Cloud Storage, Firestore
Processing
: Dataproc (PySpark), Dataflow (batch & stream)
Ingestion
: Pub/Sub, Cloud Functions, Cloud Run
Serving
: Vertex AI Online Prediction, TF Serving, Kubernetes-based custom APIs, Cloud Run
CI/CD & IaC
: GitHub Actions, GitLab CI

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