Artificial Intelligence Engineer

About the Company
We are seeking a highly skilled, hands-on Senior AI Engineer to lead the design, development, and production deployment of advanced AI solutions—spanning traditional machine learning, deep learning, and Generative AI. You will own end-to-end AI initiatives, from architecture through optimization, deployment, and monitoring, and build scalable, enterprise-grade applications such as conversational AI assistants and Retrieval-Augmented Generation (RAG) pipelines that deliver measurable business value.
About the Role
The Senior AI Engineer will lead the design, development, and production deployment of advanced AI solutions, owning end-to-end AI initiatives from architecture through optimization, deployment, and monitoring, and building scalable, enterprise-grade applications such as conversational AI assistants and Retrieval-Augmented Generation (RAG) pipelines that deliver measurable business value.
Responsibilities
Design, train, and evaluate ML and deep learning models (RNNs, GRUs, LSTMs, and Transformers such as BERT, T5, GPT) for classification, anomaly detection, forecasting, and NLP tasks.
Architect and develop Generative AI and RAG solutions for document search, conversational Q&A, and summarization using frameworks like LangChain and LlamaIndex.
Implement vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques for grounded, context-aware responses.
Optionally fine-tune LLMs using SFT and PEFT methods (LoRA, QLoRA) on domain-specific datasets.
Optimize models via quantization (dynamic/static, INT8) to improve latency and reduce compute overhead.
Deploy models into production on cloud platforms (AWS, Azure, GCP) using containerization (Docker, Kubernetes), collaborating with DevOps on CI/CD pipelines for scalability and reliability.
Define and track technical and business metrics; monitor model drift and performance, and retrain as needed.
Collaborate with cross-functional teams (data engineering, backend, DevOps, product) and mentor junior engineers; write clean, reproducible, well-documented code.
Qualifications
Bachelor's or Master's in Computer Science, Data Science, or a related field.
5+ years of hands-on experience in machine learning, AI engineering, or data science, with proven production deployment experience.
Proficiency in programming languages such as Python, Java, or C++.
Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) and traditional machine learning algorithms, especially for sequence and NLP tasks.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Proficiency with ML/DL libraries (scikit-learn, pandas) and Transformer models / open-source LLMs (e.g., Hugging Face).
Practical experience with GenAI tools, RAG frameworks, vector stores, and embeddings.
Experience with model quantization, evaluation using statistical and business metrics, and production monitoring.
Familiarity with MLflow and CI/CD practices.
Excellent problem-solving and communication skills; able to work independently and collaboratively.
Required Skills
Hands-on experience in machine learning, AI engineering, or data science with proven production deployment experience.
Proficiency in programming languages such as Python, Java, or C++.
Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) and traditional machine learning algorithms, especially for sequence and NLP tasks.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Proficiency with ML/DL libraries (scikit-learn, pandas) and Transformer models / open-source LLMs (e.g., Hugging Face).
Practical experience with GenAI tools, RAG frameworks, vector stores, and embeddings.
Experience with model quantization, evaluation using statistical and business metrics, and production monitoring.
Familiarity with MLflow and CI/CD practices.
Excellent problem-solving and communication skills; able to work independently and collaboratively.
Preferred Skills
Experience fine-tuning LLMs (SFT, LoRA, QLoRA) on domain-specific datasets.
Exposure to MLOps platforms (e.g., SageMaker, Vertex AI, Kubeflow).
Familiarity with distributed data processing (e.g., Spark, Hadoop) and orchestration tools (e.g., Airflow).
Experience building enterprise-grade conversational or agentic AI solutions.
Familiarity with computer vision and version control (Git).
Contributions to research papers, blog posts, or open-source projects in ML/NLP/GenAI.
Pay range and compensation package
INR 3500000 - 3800000

Work arrangement
No