Job Summary:

We are seeking a skilled GenAI Engineer with expertise in Large Language Models (LLMs), Generative AI, MLOps, and modern data platforms. The ideal candidate will have hands-on experience in building, integrating, and deploying AI-powered solutions while working with cloud-native data ecosystems. This role requires strong technical expertise in LLM orchestration, AI integrations, data engineering, and cloud technologies.

Key Responsibilities:

- Design, develop, and deploy Generative AI and LLM-based applications for enterprise use cases.

- Build and integrate AI solutions using Genie or equivalent LLM platforms.

- Develop AI workflows using Model Context Protocol (MCP) or similar orchestration frameworks.

- Integrate LLMs with enterprise applications, APIs, and modern analytics platforms.

- Design prompt engineering strategies to improve AI application accuracy and performance.

- Implement and maintain CI/CD pipelines for AI/ML solutions following MLOps best practices.

- Collaborate with data engineering teams to build scalable data pipelines supporting AI workloads.

- Work with modern analytics platforms such as Databricks or Microsoft Fabric for AI-driven analytics.

- Ensure AI applications comply with enterprise data governance, security, and compliance standards.

- Optimize AI models, monitor performance, and continuously improve production deployments.

Required Skills:

- 5+ years of experience in AI/ML, Data Engineering, or related domains.

- Strong hands-on experience with Generative AI and Large Language Models (LLMs).

- Experience building solutions using Genie or equivalent LLM platforms.

- Hands-on experience with Model Context Protocol (MCP) or similar orchestration frameworks.

- Strong understanding of MLOps, including CI/CD pipelines for ML/AI applications.

- Experience with Python and modern AI/Data frameworks.

- Working knowledge of data engineering concepts, including:

1. ETL Processes

2. Data Pipelines

3. Cloud Data Platforms

- Experience working with AWS Cloud.

- Strong analytical, problem-solving, and debugging skills.

Must-Have Skills:

- Hands-on experience with Databricks, Microsoft Fabric, or other modern analytics platforms.

- Experience integrating LLMs into enterprise applications.

- Knowledge of Prompt Engineering, Retrieval-Augmented Generation (RAG), or AI Copilot implementations.

- Strong understanding of AI data governance, security, privacy, and responsible AI practices.

Preferred Skills:

- Experience with vector databases and semantic search.

- Exposure to LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.

- Experience with REST APIs, microservices, and enterprise integrations.

- Familiarity with Docker, Kubernetes, and containerized AI deployments.

- Knowledge of Azure OpenAI, Amazon Bedrock, or Google Vertex AI.

Good to Have:

- Experience deploying production-grade GenAI solutions at enterprise scale.

- Understanding of distributed data processing using Spark.

- Exposure to Agile/Scrum development methodologies.

- Relevant certifications in AWS, Databricks, Microsoft Fabric, or Generative AI.