•⁠ ⁠Writing production level, scalable code with Python (or scala) •⁠ ⁠MLOps/LLMOps, machine learning engineering, Big Data, or a related role. •⁠ ⁠Elasticsearch, SQL, NoSQL, Apache Airflow, Apache Spark, Kafka, MLflow. •⁠ ⁠Containerization, Kubernetes, cloud platforms, CI/CD and workflow orchestration tools. •⁠ ⁠Distributed systems programming, AI/ML solutions architecture, Microservices architecture experience. •⁠ ⁠Show Progressive experience as in machine learning, data engineering or similar roles. •⁠ ⁠2-3 years of experience with operationalizing data-driven pipelines for large scale batch and stream processing analytics solutions •⁠ ⁠Experience with contributing to open-source initiatives or in research projects and/or participation in Kaggle competitions •⁠ ⁠Experience with SageMaker and/or Vertex AI or Azure AI Foundry or Databricks Highly Desirable Experience: •⁠ ⁠6-12 months of experience working with RAG pipelines with vector databases (FAISS, Pinecone, Weaviate, etc.), prompt engineering and/or Generative AI use cases. •⁠ ⁠Fine-tune transformer-based models for domain-specific tasks •⁠ ⁠Develop AI agents that can perform task chaining and tool usage (e.g., LangGraph, Auto-GPT, AgentOps)