GEN AI

We are looking for a skilled Generative AI Engineer to design, build, and deploy production-grade AI applications.

Role & responsibilities

In this role, you will bridge the gap between frontier AI model capabilities (such as OpenAI, Anthropic, Gemini, and open-source LLMs like Llama) and real-world enterprise software. You will work cross-functionally with product, data, and infrastructure teams to embed intelligent features, RAG architectures, and autonomous agents into our core product ecosystem.

Key Responsibilities LLM Integration: Design and build scalable applications integrating LLM APIs and open-source models across multiple providers.

RAG & Vector Search: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines, leveraging vector databases

Agentic Frameworks: Develop multi-agent workflows and autonomous execution loops using frameworks like Lang Chain, Lang Graph, or LlamaIndex.

Fine-Tuning & Optimization: Fine-tune foundation models or implement prompt engineering, in-context learning, and quantization to improve accuracy and lower latency

Production Deployment (MLOps): Containerize AI services via Docker and Kubernetes, deploying reliable pipelines on cloud platforms (AWS, GCP, or Azure).

Safety & Guardrails: Implement robust guardrails, evaluation frameworks (e.g., Ragas, TruLens), and data privacy protocols to ensure secure and ethical AI outputs.

Preferred Qualifications Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.

Experience optimizing inference latency and reducing token consumption costs at scale.

Contributions to open-source AI repositories or published research/projects in NLP or LLMs.