Description :

Machine Learning Engineer Recommender Systems | 4-8 Years | Bangalore (Hybrid) | Contract

Were looking for passionate and skilled Machine Learning Engineers experienced in Recommendation Systems to join our clients dynamic team!

If you love building intelligent personalization systems, working on large-scale data, and solving real-world ML challenges this role is for you!

Position Details

Location : Bangalore (Hybrid)

Experience : 4-8 Years

Bill Rate : As per market standards

Hiring Type : Contract.

Client Interview : Yes

Role Overview :

As a Machine Learning Engineer (Recommender Systems), youll design, build, and optimize models that drive personalized experiences for millions of users.

Youll work with cutting-edge tools, data pipelines, and neural architectures to power real-time recommendations and product ranking engines.

Key Responsibilities :

- Build end-to-end recommendation systems collaborative filtering, content-based, and neural recommenders (Two-Tower / Dual Encoder).

- Develop and optimize embedding-based retrieval and similarity search.

- Design scalable ML pipelines using Python, TensorFlow, PyTorch, Scikit-learn.

- Work with large-scale distributed data systems (Spark, BigQuery).

- Define and track evaluation metrics (Recall@K, NDCG, MAP).

- Apply Computer Vision (image embeddings) & NLP (product text embeddings).

- Build real-time search & personalization engines.

- Hands-on with Predictive & ML models deployment.

- Use cloud platforms (AWS / GCP / Azure).

- Collaborate across teams to drive AI-powered innovation in fashion retail & e-commerce.

Required Skills :

- Bachelors / Masters / PhD in Computer Science, Data Science, or Machine Learning.

- Strong background in Recommendation Systems Collaborative Filtering, Neural Networks, Embedding Models.

- Proficiency in Python, SQL & ML frameworks (TensorFlow, PyTorch, Scikit-learn).

- Experience with Spark, BigQuery, and distributed computing.

- Cloud knowledge : AWS / GCP / Azure.

- Exposure to CV + NLP, real-time personalization, and predictive modeling.

- Domain : Fashion Retail / E-commerce personalization (preferred).

Nice to Have :

- Familiarity with Vector Databases (FAISS, Milvus, etc.)

- Exposure to MLOps & CI/CD automation

- Published research or open-source ML project contributions

Why Join Us :

- Work on high-impact ML systems influencing millions of users

- Exposure to latest AI/ML stacks & large-scale data

- Collaborative & innovative environment

- Opportunity to deploy your models in real business use cases