Job Overview
Role: AI/Machine Learning Engineer Location: Gurugram, Haryana, India Experience: 0 – 3 Years Qualification: Bachelor’s degree in Computer Science, IT, or a related field Key Skills: AI/ML, Python, NLP, GenAI, PyTorch/TensorFlow
Job Description
As an AI/Machine Learning Engineer, you will collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to design, develop, and maintain robust machine learning models and workflows. Your work will involve optimizing algorithms for performance and integrating AI capabilities into dynamic applications to provide seamless user experiences. You will be part of the central Digital Products and Solutions (DPS) organization, within the Software Application and Engineering department, which develops software solutions for both internal and external customers across categories like Asset Performance Management, Energy Management, and AI-assisted Applications.
Roles and Responsibilities
- ML & GenAI Pipelines: Assist in building and maintaining pipelines for data preprocessing, training, evaluation, and inference.
- NLP & RAG Workflows: Support the development of simple RAG-based and NLP workflows, handling text processing tasks like data cleaning, parsing, chunking, and embeddings.
- Backend Integration: Contribute to backend APIs (e.g., FastAPI) to expose ML/AI functionalities and integrate AI models into applications.
- Code Quality: Write clean, modular, and testable Python code while adhering to best software development practices.
- Optimization & Debugging: Debug, test, and optimize models and pipelines for performance and reliability in collaboration with senior engineers.
Skills and Eligibility Criteria
Educational Background: Bachelor’s degree in Computer Science, IT, or a related field.
Experience: 0–3 years of experience or a strong academic/project background in AI/ML.
Mandatory Technical Skills:
- Strong programming skills in Python with solid problem-solving abilities.
- Basic understanding of ML concepts, NLP, model evaluation, and backend API development.
- Familiarity with GenAI (LLMs, prompt engineering, RAG) is a strong plus.
- Exposure to NumPy, Pandas, scikit-learn, PyTorch/TensorFlow, and Git.
- Exposure to AWS or Azure is advantageous.