Opening For AI Engineer

Role & responsibilities

• AI Architecture:

  Architect and scale production-grade RAG pipelines, managing everything from document ingestion and advanced chunking strategies to vector database optimization, hybrid search, and multi-model LLM integration.

• Model Integration & Prompting:

  Integrate leading commercial and open-source LLMs (OpenAI, Anthropic, Meta Llama, etc.) into applications using advanced prompt engineering techniques.

• Application Development:

  Design, develop, test, and maintain robust software applications based on business and technical requirements.

• Agentic Workflows:

  Develop multi-agent systems and tools for LLMs to execute complex workflows.

• Code & Quality:

  Write clean, scalable, and efficient code following coding standards and best practices, while performing code reviews to ensure adherence to security and performance standards.

• System Integration:

  Integrate third-party APIs, databases, and external systems as required.

• Collaboration:

  Collaborate with business stakeholders, product teams, and QA teams to understand project requirements and deliver effective solutions.

• Compliance & Security:

  Ensure applications comply with organizational security, compliance, and data protection standards, particularly regarding sensitive data passed to AI models.

Preferred candidate profile

• Experience with cloud platforms (AWS, Azure, or Google Cloud) and their respective AI services (e.g., Azure OpenAI, AWS Bedrock, GCP Vertex AI).
• Experience with ML Ops and LLM observability tools (e.g., LangSmith, Weights & Biases, MLflow).
• Knowledge of front-end technologies such as HTML, CSS, JavaScript, React, or Angular to build quick AI prototypes.
• Familiarity with Docker, Kubernetes, and CI/CD pipelines