Design, develop, and deploy machine learning models and end-to-end ML pipelines.
Work with large structured and unstructured datasets to perform data preprocessing, feature engineering, and exploratory analysis.
Build, fine-tune, and evaluate supervised, unsupervised, and deep learning models for various use cases.
Develop and implement scalable inference systems, APIs, and microservices for production.
Collaborate with Data Engineering, Product, and Software teams to integrate models into products and platforms.
Research and implement state-of-the-art techniques in NLP, computer vision, and generative AI where applicable.
Monitor model performance, automate retraining workflows, and ensure model reliability and accuracy.
Optimize models for performance, scalability, and low-latency inference.
Create clear technical documentation, model cards, and deployment guidelines.
Mentor junior engineers and support code reviews and architecture discussions.

Required Skills & Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
5+ years of hands-on experience
in machine learning, deep learning, or AI engineering.
Strong proficiency in
Python
and ML/DL frameworks such as
TensorFlow, PyTorch, Scikit-learn
.
Solid experience with
data pipelines
,
ETL
, and working knowledge of SQL/NoSQL databases.
Experience building and deploying models using
AWS / Azure / GCP
services.
Strong understanding of
ML Ops
, CI/CD, containerization (Docker), and orchestration (Kubernetes).
Expertise in at least one key area such as
NLP, Computer Vision, Time-Series Forecasting, or Generative AI
.
Working knowledge of version control (Git), model versioning, and experiment tracking tools such as
MLflow, DVC
, or similar.
Ability to write clean, optimized, and production-quality code.
Strong problem-solving abilities, analytical skills, and a deep understanding of ML algorithms.

Preferred Skills

Experience with
LLMs
, prompt engineering, vector databases, or retrieval-augmented systems (RAG).
Familiarity with big data technologies (Spark, Hadoop).
Experience with Bayesian optimization, AutoML frameworks, or hyperparameter tuning tools.
Exposure to microservice-based architectures.

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