AI & Machine Learning Engineer

Tart Labs; Coimbatore District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Tart Labs in India seeks a seasoned AI & Machine Learning Engineer to design, develop, and deploy scalable AI/ML solutions for an EdTech platform. The role requires a Master's in AI/ML or related field and 5+ years of experience with LLMs, RAG, vector databases, and modern ML frameworks.

You will work with cross-functional teams to integrate AI into real-world applications, build production-ready pipelines, and ensure ethical, explainable models with robust monitoring and retraining processes.

Qualifications

• Master's degree in AI, ML, Data Science, CS, or related field (mandatory).
• 5+ years in AI/ML engineering roles, preferably in EdTech or learning analytics.
• Proficient in Python and ML libraries; knowledge of R or Java is a plus.

Responsibilities

• Design and develop scalable AI/ML models for NLP, CV, analytics, and recommendations in EdTech.
• Fine-tune LLMs using LoRA, QLoRA, and PEFT methods for domain use cases.
• Build RAG pipelines using vector databases (Pinecone, Weaviate, Faiss).
• Implement prompt engineering for content delivery, doubt resolution, and assessments.
• Integrate OpenAI, Claude, Gemini, Llama models into the platform.
• Leverage LangChain, Transformers, TensorFlow, PyTorch, Scikit-learn, Keras.
• Work with real-time and batch data pipelines (Spark, Kafka, Databricks, Hadoop).
• Deploy models to production on AWS, GCP, Azure; use Docker & Kubernetes.
• Set up CI/CD and MLOps for monitoring (MLflow, Weights & Biases) and retraining.
• Optimize inference for cloud/edge; explainability with SHAP/LIME.

Skills

Python

NumPy

Pandas

Scikit-learn

LLMs

RAG

Prompt engineering

LangChain

Hugging Face Transformers

Education

Master's degree in AI/ML/Data Science

Tools

TensorFlow

PyTorch

Keras

LangChain

Weaviate/Pinecone/Faiss

Docker

Kubernetes

Job description

AI & Machine Learning Engineer

We are seeking a highly skilled AI & Machine Learning Engineer to design, develop, and deploy intelligent AI/ML solutions for our EdTech platform. The ideal candidate must have a Master's degree in Artificial Intelligence, Machine Learning, or a related field and expertise in the latest AI methodologies, including Retrieval-Augmented Generation (RAG), Generative AI, Large Language Models (LLMs), and Multimodal AI. The role involves building scalable models, working with large datasets, and integrating AI capabilities into real-world applications.

Key Responsibilities

• Design and develop scalable AI/ML models for NLP, Computer Vision, Predictive Analytics, and Recommendation Engines tailored to the EdTech domain.

• Fine-tune Large Language Models (LLMs) using LoRA, QLoRA, and PEFT methods for domain-specific use cases.

• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, or Faiss.

• Implement prompt engineering strategies for intelligent content delivery, doubt resolution, and personalized assessments.

• Integrate third-party and open-source AI models (e.g., OpenAI GPT-4, Claude, Gemini, Llama) into the learning platform.

• Leverage LangChain, Hugging Face Transformers, and modern ML frameworks (TensorFlow, PyTorch, Scikit-learn, Keras) for development.

• Work with real-time and batch data pipelines using tools like Apache Spark, Kafka, Databricks, and Hadoop.

• Deploy AI models to production using cloud platforms (AWS SageMaker, Google Vertex AI, Azure ML) and tools like Docker and Kubernetes.

• Set up and manage CI/CD pipelines and adopt MLOps best practices for automated deployment, monitoring (MLflow, Weights & Biases), and retraining.

• Optimize inference for cloud/edge deployment and implement model explainability (e.g., SHAP, LIME).

• Collaborate with cross-functional teams including instructional designers, data engineers, and full-stack developers.

• Continuously explore and experiment with the latest advancements in AI for education, ensuring ethical, explainable, and equitable models.

• Document experiments, model architectures, performance benchmarks, and deployment processes for reproducibility and audit readiness.

Requirements

• Education: Master's degree in AI, Machine Learning, Data Science, Computer Science, or a related field (mandatory).

• Experience: 5+ years in AI/ML engineering roles, preferably in EdTech or learning analytics domains.

• Languages & Libraries: Expert in Python (NumPy, Pandas, Scikit-learn). Knowledge of R or Java is a plus.

• Frameworks: Strong experience with TensorFlow, PyTorch, Keras, Hugging Face Transformers, and LangChain.

• LLMs & RAG: Hands-on with LLM fine-tuning, prompt engineering, RAG pipelines, and vector search engines.

• Cloud & Big Data: Solid experience with AWS, GCP, Azure, and platforms like Databricks, Kafka, Spark, Hadoop.

• MLOps Tools: Familiar with Docker, Kubernetes, MLflow, Weights & Biases, and CI/CD practices.

• Visualization/Prototyping: Exposure to FastAPI, Flask, Gradio, or Streamlit is a bonus.

• Math & Stats: Strong understanding of statistics, probability, linear algebra, and deep learning fundamentals.

• Soft Skills: Excellent problem-solving, analytical, and communication skills. Ability to mentor junior team members is an advantage.