AI Engineer

Role Description: We are seeking an experienced and innovative AI Engineer to design, build, and deploy end-to-end Machine Learning and Generative AI solutions. In this role, you will work across the full AI lifecycle—from data ingestion and model development to deployment, monitoring, and optimization—leveraging technologies such as LLMs, RAG architectures, Agentic AI frameworks, Azure Cloud, Databricks, and Microsoft Fabric. You will collaborate closely with product, data, and engineering teams to deliver production-grade AI systems that create measurable business impact. The role includes developing LLM-powered applications, building autonomous multi-agent solutions, optimizing GPU-based performance, and ensuring governance, fairness, and compliance in deployed models. Key Responsibilities: Design, train, and deploy machine learning and deep learning models (NLP, vision, recommendation, predictive). Build end-to-end ML systems from data ingestion to production. Develop and implement LLM applications, RAG pipelines, and domain-specific copilots using LangChain, CrewAI, and Azure OpenAI. Build Agentic AI systems with multi-agent reasoning, memory, and collaborative decision-making. Work with Data Engineers to prepare and transform data using Azure Fabric, Data Lakes, Spark, and Databricks. Deploy and monitor models using MLflow, Azure ML, Databricks, CI/CD pipelines, Docker, and Terraform. Implement model governance, guardrails, A/B testing, and responsible AI practices. Optimize model training and inference using GPU frameworks (CUDA, TensorRT, NVIDIA Triton). Collaborate with cross-functional teams to translate requirements into scalable AI solutions. Qualifications Required 3–6 years of experience in AI/ML development, Generative AI, or applied data science. Proven experience building and deploying end-to-end ML solutions in production. Strong proficiency in Python, PySpark, TensorFlow, PyTorch. Hands-on experience with Azure Databricks, MLflow, LangChain, CrewAI, and embedding models. Experience with RAG architectures, chatbots, and document understanding systems. Skilled in MLOps, CI/CD, and cloud deployments on Azure (Azure ML, Fabric, Data Lake). Experience with Docker, Terraform, or equivalent IaC tools. Familiarity with vector databases (Pinecone, Weaviate, FAISS). Understanding of model evaluation, A/B testing, AI governance, and guardrails. Exposure to GPU-based training and distributed compute environments. Preferred Degree in Computer Science, Data Science, AI, or related field. Experience building enterprise AI copilots, RAG systems, or Agentic AI applications. Industry knowledge in eCommerce, Retail, or Supply Chain. Certifications such as Microsoft Azure AI Engineer or Databricks ML. Soft Skills Strong analytical and problem-solving mindset. Excellent communication and documentation abilities. Entrepreneurial, proactive, and ownership-driven. Team-oriented and effective in agile, fast-paced environments.