AI Engineering

About the role

We are looking for an AI Enginee r with hands-on experience designing and deploying scalable AI solutions. In this role, you will be part of a cross-functional team working on cutting-edge projects involving Retrieval-Augmented Generation (RAG), agentic frameworks, and end-to-end MLOps workflows. You'll play a key role in developing AI applications using tools like LangChain, CrewAI, and Google ADK, while applying advanced prompt engineering techniques and ensuring robust monitoring and performance tracing. Your collaboration with other engineering will help align innovative AI systems with broader development goals. What is this position about? Design, develop, and deploy AI solutions. Implement Retrieval-Augmented Generation (RAG) and fine-tuning techniques. Utilize and integrate AI frameworks like LangChain. Use advanced prompt engineering techniques to solve complex problems with LLMs. Build and manage agentic frameworks such as CrewAI and Google ADK. Apply MLOps best practices to streamline AI development workflows. Monitor and trace AI applications performance. Collaborate across teams to ensure alignment with traditional ML and AI methodologies. Work on end-to-end software engineering for scalable solutions. 4+ years of experience in building and deploying AI/ML or GenAI solutions in production environments. Strong experience with at least one major cloud platform (Azure, AWS, or GCP) for building and deploying AI/ML solutions. Experience with services such as Azure ML, AWS Bedrock/SageMaker, GCP Vertex AI, Databricks, or equivalent AI/ML platforms is preferred. Proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn). Hands-on experience with RAG architecture, prompt engineering, and LLM-based application development. Experience with MLOps/LLMOps pipelines, model tracking, observability, and CI/CD using tools such as Azure DevOps, Jenkins, GitHub Actions, GitLab CI/CD, MLflow, or equivalent platforms. Familiarity with enterprise data integration and orchestration tools such as Azure Data Factory, Synapse, Databricks, Airflow, Kafka, or equivalent distributed data platforms. Strong backend engineering and API development experience using FastAPI, Flask, Node.js, or similar frameworks, with ability to build scalable AI-powered services and microservices. Excellent problem-solving, debugging, and collaboration skills. Good to Have Experience building production-grade RAG systems using vector databases such as Pinecone, FAISS, ChromaDB, pgvector, Milvus, or Azure AI Search. Experience with agentic AI frameworks such as LangGraph, CrewAI, AutoGen, MCP, or Google ADK. Familiarity with AI observability and evaluation tools such as LangSmith, Langfuse, RAGAS, MLflow, App Insights, or Prometheus/Grafana. Experience deploying AI systems using Docker, Kubernetes, serverless architectures, or container orchestration platforms.