Role Overview :

We are looking for a highly skilled Machine Learning / Generative AI Engineer to design, develop, and deploy intelligent AI-driven solutions. The ideal candidate will have hands-on experience across the full ML lifecycle, including LLM-powered applications, scalable deployments, and close collaboration with cross-functional teams to deliver high-quality solutions for diverse client needs.

Experience Required : 03+ Years

Key Responsibilities :

- Architect, develop, and operationalize machine learning and deep learning models for real-world business use cases.

- Build and integrate LLM-driven solutions such as embeddings, semantic search, vector-based retrieval, and RAG workflows.

- Create efficient data preparation pipelines, including feature engineering and transformation processes.

- Establish and manage MLOps practices such as experiment tracking, automated pipelines, CI/CD, and production-grade deployments.

- Work with vector databases and similarity search frameworks to support AI-driven applications.

- Partner with engineering, QA, and delivery teams to translate client requirements into robust AI solutions.

- Continuously fine-tune and optimize models for accuracy, scalability, performance, and reliability.

Required Skills & Qualifications :

- Strong programming expertise in Python with solid working knowledge of SQL.

- Proven experience using machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.

- Hands-on exposure to Large Language Models, Generative AI platforms, APIs, and prompt engineering techniques.

- Practical experience with orchestration frameworks like LangChain, LlamaIndex, or equivalent tools.

- Strong fundamentals in NLP, deep learning architectures, transformers, and neural networks.

- Experience working with at least one major cloud platform (AWS, Azure, or GCP).

- Familiarity with containerization, version control, and API development (Docker, Git, RESTful services).

Preferred Qualifications :

- Experience with tools such as Kubernetes, Airflow, MLflow, or Weights & Biases.

- Demonstrated ability to deploy and maintain AI/ML solutions in production environments.

- Exposure to computer vision, multimodal models, or cross-domain AI applications.

- Prior experience working in a service-based or offshore delivery model is a plus.