Senior Artificial Intelligence Engineer

Position Overview

We are seeking a highly skilled, hands-onSenior AI Engineerto lead the design, development, and production deployment of advanced AI solutionsspanning 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.

Key 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.

Required Qualifications

• Bachelor's or Master's in Computer Science, Data Science, or a related field.
• 5+ yearsof 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 Qualifications

• 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.