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.