AI/ML Engineer

About the Role

We are seeking an experienced

AI/ML Engineer

with a strong background in designing, developing, and deploying machine learning models and AI-driven solutions. The ideal candidate will have hands-on expertise across the full ML lifecycle—from data exploration and feature engineering to model training, optimization, and production deployment. You will work closely with cross-functional teams to deliver scalable, high-impact AI solutions.

Key Responsibilities

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.