Gen AI Engineer

We are seeking a talented and driven GenAI Engineer with 3–6 years of experience to join our dynamic team. In this role, you will leverage your analytical skills, machine learning expertise, and Generative AI capabilities to manage Gen AI pipelines / workflows, extract insights from unstructured datasets, build intelligent AI solutions, and contribute to the development of innovative, scalable systems that enhance our products and services.

Responsibilities

• Manage GenAI models production pipelines to debug defects and provide root cause assessments.
• Build and orchestrate LLM‑based workflows using frameworks such as LangChain, including prompt engineering and pipeline design.
• Implement Retrieval‑Augmented Generation (RAG) architectures leveraging vector databases such as Pinecone for semantic search and contextual retrieval.
• Work with document ingestion and extraction workflows, including processing unstructured documents (PDFs, scans, forms) using tools like AWS Extract and GenAI‑based extraction techniques.
• Collaborate with cross‑functional teams (engineering, product, and business stakeholders) to define data requirements, evaluation metrics, and success criteria.
• Communicate findings and insights effectively to both technical and non‑technical audiences through visualizations, reports, and presentations.
• Stay current with industry trends, tools, and best practices in Generative AI, LLMs, data science, and analytics.
• Mentor junior team members and contribute to a culture of continuous learning and technical excellence.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or a related field.
• 3–6 years of experience in a data science, applied ML, or GenAI role, with a strong portfolio of projects.
• Hands‑on experience with machine learning frameworks (scikit‑learn, TensorFlow, PyTorch).
• Practical experience with LLMs, GenAI frameworks, LangChain, and prompt‑driven workflows.
• Strong understanding of RAG patterns, vector embeddings, and vector databases such as Pinecone.