Lead Generative AI Engineer (Full-Stack)

Job Title:-Lead Generative AI Engineer (Full-Stack)

Purpose: Partner directly with our Lead Generative AI Engineer to operationalize our AI vision. You will build the robust APIs, data pipelines, and front-end integrations that bring our generative models to life for users.

Role Summary

We are seeking a versatile and hands-on engineer who is passionate about the intersection of AI and software development. You are comfortable moving across the tech stackfrom processing data in Python and building backend APIs in .NET, Java, or Node.js, to integrating results into a UI. You will be the crucial link between our core AI models and our end-user applications. 🛠️

Key Responsibilities

• Implement Data & Embedding Pipelines: Build, scale, and maintain the data ingestion and vector embedding pipelines that feed our RAG (Retrieval-Augmented Generation) systems, working with data from sources like Kafka and Postgres/MSSQL.
• Develop Scalable AI APIs: Construct and deploy high-performance APIs (using frameworks like FastAPI, .NET Web API, or Node.js) to serve search results and LLM-generated content to our applications.
• Integrate and Experiment: Collaborate with the Lead AI Engineer and front-end teams to integrate AI-powered features into our user interface. You will help set up and run A/B tests to measure impact.
• Ensure Observability: Instrument all services with OpenTelemetry and contribute to our LLMOps framework by pushing detailed metrics to Prometheus and Grafana, helping us monitor performance, cost, and quality.

Must-Have Skills

• Hybrid Experience: 3+ years of professional experience, demonstrating a strong foundation in Python for data processing/AI tasks, PLUS proficiency in a backend language such as C# (.NET), Java, or Node.js.
• Data & Messaging: Solid experience with SQL (Postgres/MSSQL) and a strong understanding of message queue patterns using Kafka (both consumer and producer).
• Search Technology: Hands-on experience with search platforms like OpenSearch or Elasticsearch, including index management and writing complex queries.
• Engineering Fundamentals: Proficiency with Docker for containerization and core Git workflows.

Nice-to-Have Skills

• Exposure to Generative AI frameworks like LangChain or Hugging Face Transformers.
• Familiarity with vector databases and vector search concepts.
• Front-end experience with React or Next.js.
• Experience with CI/CD tools like Azure Pipelines or GitHub Actions.
• Basic understanding of Kubernetes.