Embark on a mission as a Software Detective. This role offers a dynamic service focused on identifying and resolving intricate issues across various components of critical business systems. You'll be instrumental in ensuring the smooth operation of essential platforms.

We are actively seeking a skilled Generative AI Engineer. The ideal candidate will possess extensive hands-on experience with Databricks and AWS, crucial for designing, developing, and scaling advanced LLM-powered, agentic AI solutions tailored for enterprise applications. This position involves building sophisticated intelligent agents, robust RAG pipelines, and cutting-edge AI-driven data products that are not only production-ready but also scalable, secure, and cost-effective.

Key responsibilities include designing and developing advanced GenAI and Agentic AI solutions utilizing LLMs for diverse enterprise use cases. You will build comprehensive RAG pipelines that effectively leverage both structured and unstructured data, including documents, PDFs, logs, and metadata. The role also involves implementing AI agents and complex workflows using AWS Agent Core / Bedrock and various orchestration frameworks. You will develop and deploy GenAI applications on Databricks, utilizing notebooks, jobs, and scalable compute resources. Furthermore, you will integrate these GenAI solutions seamlessly with data lakes, lakehouse architectures, and existing enterprise systems, optimizing them for performance metrics such as latency, accuracy, cost, and scalability. Mastering prompt engineering, prompt chaining, and implementing effective guardrails are essential components of this role. Collaboration with data engineers, architects, and business stakeholders is vital to translate requirements into actionable AI-driven solutions. You will also contribute to AI governance, observability, and responsible AI practices, supporting deployment, monitoring, and continuous improvement of GenAI systems in production environments.

Essential technical proficiencies encompass a strong understanding of Generative AI and LLMs, with hands-on experience using various LLMs such as Claude, OpenAI, and Llama. A deep knowledge of RAG architectures, embeddings, and vector search is mandatory, alongside experience in building agentic workflows involving planning, tool usage, and reasoning loops. Expertise in prompt engineering, few-shot, and chain-of-thought techniques is required. Significant experience with the Databricks Lakehouse platform, including PySpark, SQL, and Delta Lake, is crucial. Familiarity with Databricks workflows/jobs and MLflow for experimentation and model tracking is expected. Experience with Unity Catalog is a plus. On the AWS front, proficiency with AWS Bedrock (models, inference, agents), AWS Agent Core or similar agent orchestration patterns, Lambda, API Gateway, and S3 is necessary. A solid understanding of IAM and security best practices for AI workloads is also a requirement. Advanced Python and SQL programming skills, along with experience in APIs and microservices integration, are fundamental. Beneficial skills include experience with frameworks like LangChain, LangGraph, or DSPy, familiarity with vector databases (OpenSearch, FAISS, Pinecone), and exposure to LLMOps/MLOps. Knowledge of CI/CD for AI applications and experience in client-facing or consulting environments are advantageous.