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.