AI / ML Engineer - Databricks

• Total Experience: 4 to 5 years of hands-on experience in Machine Learning Engineering, AI Engineering, or MLOps.
• Proven experience in building and deploying at least 2 end-to-end ML/AI solutions in a production environment.
• Agentic/Gen AI Practical, hands-on experience with Generative AI and Agentic AI frameworks is highly preferred.
• Education: Bachelor's or Master's degree in Computer Science/Data Science/AI.

Mandatory Skills:

• Programming: Expert proficiency in Python (including libraries like NumPy, Pandas, and Scikit-learn).
• ML/DL Frameworks: Hands-on experience with PyTorch or TensorFlow (and Keras).
• Generative AI: Experience with LLMs (e. g., OpenAI, Gemini, and Llama) and core concepts like embeddings, tokenization, and fine-tuning.
• Agentic Frameworks: Proven experience with at least one Agent Orchestration Framework (e. g., LangChain, LangGraph, AutoGen).
• MLOps Tools: Practical experience with key ML Ops components: Docker, Kubernetes, and an
• MLOps platform/tool (e. g., MLflow, Kubeflow, DVC).
• Cloud Platform: Proficiency in deploying and managing AI/ML workloads on a major cloud platform (Databricks, AWS SageMaker, Google Cloud Vertex AI, or Azure ML).
• Databases: Strong knowledge of SQL and experience with Vector Databases (e. g., Pinecone, Weaviate). Familiarity with data engineering (Spark, SQL, ETL pipelines).

Good to Have Skills:

• Advanced MLOps: Experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions) and sophisticated monitoring tools (Prometheus, Grafana, Datadog).
• Big Data: Familiarity with distributed computing frameworks like Apache Spark.
• Front-End Integration: Experience with creating APIs (FastAPI, Flask) and integrating ML services with front-end applications.
• Other AI: Experience with Computer Vision or Time-Series Analysis in a production setting.
• Soft Skills: Strong communication skills for CoE evangelism, cross-functional collaboration, and presenting POC results to stakeholders.
• Experience with RAG Architectures.
• Exposure to Databricks Unity Catalog, DLT, Delta Live Tables, AutoML, and Feature Stores.
• Understanding of security, compliance, and responsible AI practices.