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