1 opening₹4,50,000 – ₹5,50,000 per year Delhi, India

About the role

AI Engineer

Job Title

AI Engineer

Employment Type

Full-Time

Experience

2–5 years of relevant experience in AI/ML engineering
(Freshers with strong hands-on AI/ML projects may also be considered.)

Role Overview

We are looking for an AI Engineer to design, develop, integrate, and optimize AI/ML systems for our products. The role involves working across Generative AI, LLMs, NLP, machine learning, data pipelines, model inference, and AI-powered automation.

The ideal candidate should have strong programming skills and practical experience building and deploying AI solutions in production environments.

Key Responsibilities

Design and develop AI/ML models and intelligent application features.
Build and integrate LLM-based applications, including prompt engineering, structured outputs, RAG, embeddings, and vector search.
Develop NLP pipelines for text classification, extraction, summarization, entity recognition, and semantic analysis.
Work with AI/ML frameworks and libraries such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain/LlamaIndex, or equivalent technologies.
Integrate OpenAI, Gemini, Claude, Llama, Ollama, or other LLM/model APIs based on product requirements.
Build model inference pipelines and optimize latency, accuracy, scalability, and cost.
Develop data preprocessing, feature engineering, training, evaluation, and validation pipelines.
Work with structured and unstructured datasets and implement appropriate data-processing workflows.
Design and maintain AI inference APIs and microservices using technologies such as Python, FastAPI, Flask, or equivalent.
Implement RAG pipelines using embeddings, vector databases, document retrieval, and context management.
Evaluate AI outputs using appropriate accuracy, relevance, hallucination, latency, and quality metrics.
Monitor AI systems in production and troubleshoot model, data, and inference-related issues.
Collaborate with backend, frontend, DevOps, and product teams to integrate AI capabilities into production systems.
Research and evaluate emerging AI models, frameworks, APIs, and techniques.
Implement appropriate AI security, data privacy, access control, and responsible-AI practices.

Required Technical Skills

Requirements

Programming Strong proficiency in Python Good understanding of REST APIs and backend development Familiarity with Git/GitHub and software development practices Machine Learning Supervised and unsupervised learning Classification, regression, clustering, and anomaly detection Model evaluation and optimization Feature engineering and data preprocessing Deep Learning Neural networks and deep learning fundamentals Experience with PyTorch or TensorFlow Familiarity with model training, fine-tuning, and inference Generative AI / LLM LLM APIs and open-source models Prompt engineering RAG architecture Embeddings and vector search Function/tool calling Structured output generation Model evaluation Fine-tuning/LoRA is a plus NLP Text preprocessing Text classification Named Entity Recognition Semantic similarity Information extraction Summarization Databases & Infrastructure SQL and NoSQL databases Experience with vector databases such as FAISS, Qdrant, Pinecone, Weaviate, or Chroma Docker and containerized deployments Basic cloud deployment knowledge Familiarity with CI/CD is a plus Preferred Skills Experience with Generative AI / LLM production systems Experience building AI agents or AI automation workflows Experience with MLOps Knowledge of model quantization and inference optimization Experience with GPU-based workloads Familiarity with cloud platforms such as AWS, Azure, or GCP Experience working with open-source LLMs and Ollama Knowledge of AI observability and evaluation frameworks Experience working in an agile product-development environment Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. Strong understanding of software engineering and machine-learning fundamentals. Strong problem-solving and analytical skills. Ability to independently research, prototype, implement, test, and deploy AI solutions. What We Look For Strong hands-on implementation skills rather than purely theoretical knowledge. Ability to convert product requirements into production-ready AI solutions. Understanding of the complete AI lifecycle: Data → Processing → Model → Inference → Evaluation → Deployment → Monitoring Ability to evaluate different AI approaches based on accuracy, performance, scalability, and cost. Strong debugging and problem-solving capabilities. Good communication and collaboration skills. Nice-to-Have Contributions to open-source AI/ML projects Published research or technical articles Kaggle or other ML competition experience Personal AI/LLM projects Experience deploying AI applications at scale Experience with multimodal AI, computer vision, speech AI, or AI agents