AI / Machine Learning Engineer

About the Role

AI / Machine Learning Engineer: Graduate (Freshers) and Experienced Engineers

Location: Remote / Hybrid

Employment Type: Full-Time

Key Responsibilities

• Design, develop, and deploy Machine Learning and Generative AI models for production use.
• Build and optimize AI-powered backend services and REST APIs using Django and FastAPI.
• Integrate Large Language Models (LLMs), NLP models, and other AI services into web applications and enterprise workflows.
• Build data preprocessing, feature engineering, training, validation, and evaluation pipelines.
• Apply prompt engineering and AI orchestration to build Generative AI applications.
• Test, benchmark, and improve model accuracy, inference latency, and scalability.
• Work with Product Managers, UI/UX Designers, Data Engineers, and Developers to turn business needs into working AI solutions.
• Document architecture, model designs, experiments, and APIs.

Requirements & Tech Stack

• Programming: Python, SQL
• AI / ML: TensorFlow, PyTorch, scikit-learn, Generative AI, LLMs, Prompt Engineering, NLP
• Backend: Django, FastAPI, REST APIs
• Data: Pandas, NumPy
• DevOps & Tools: Git
• Cloud: AWS, Microsoft Azure, Google Cloud Platform

Qualifications

Everyone

• Bachelor’s degree in Computer Science, Software Engineering, AI, Data Science, or a related field.
• Strong programming skills in Python.
• Curiosity about AI and a habit of learning new tools quickly.
• Good problem-solving, debugging, and communication skills.
• Familiarity with Git and basic software development practices.

Graduate Track (Freshers)

• For final-year students and recent graduates (0–1 year of experience).
• Academic, personal, hackathon, or internship projects using ML or LLMs (a GitHub or portfolio link is helpful).
• Basic understanding of ML concepts and a framework such as scikit-learn, TensorFlow, or PyTorch.
• Some exposure to Pandas, NumPy, SQL, or building a simple API.

Experienced Track

• Professional experience in AI/ML engineering or a related software engineering role.
• Hands-on experience with TensorFlow or PyTorch and with backend development in Django and FastAPI.
• Practical knowledge of:
• Natural Language Processing (NLP)
• Large Language Models (LLMs) and prompt engineering
• AI model integration using APIs and SDKs
• Experience with SQL databases, Pandas, and NumPy.
• Understanding of REST APIs, microservices, and backend architecture.

Bonus Points (Any Track)

• Retrieval-Augmented Generation (RAG) architectures.
• Vector databases such as Pinecone, Weaviate, Milvus, or ChromaDB.
• Orchestration frameworks such as LangChain or LlamaIndex.
• Deployment with Docker and Kubernetes.
• Cloud platforms: AWS, Azure, or Google Cloud.
• CI/CD and MLOps practices.
• AI security, responsible AI principles, and model monitoring.