Generative AI Engineer

Role Overview:

As a Generative AI Engineer, you will sit at the intersection of cutting-edge research and scalable product engineering, architecting sophisticated systems that leverage Large Language Models to solve complex business challenges. Your day-to-day involves designing and deploying Agentic AI workflows, fine-tuning LLMs for domain-specific tasks, and optimizing inference pipelines to ensure high performance and reliability. You will collaborate closely with cross-functional product teams, data scientists, and senior stakeholders to translate ambiguous business requirements into robust, production-grade AI solutions.

Key Responsibilities:

- Architect and deploy end-to-end Agentic AI frameworks that automate multi-step reasoning tasks to improve operational throughput for our business units.

- Fine-tune and optimize pre-trained LLMs using techniques like LoRA or QLoRA to ensure high accuracy and relevance for specialized industry datasets.

- Design scalable RAG (Retrieval-Augmented Generation) pipelines to provide models with real-time, context-aware data, significantly reducing hallucination rates.

- Collaborate with engineering teams to integrate AI models into existing production environments, ensuring seamless API performance and low-latency response times.

- Evaluate and benchmark model performance against business KPIs, iterating on prompt engineering and architectural design to maximize ROI for stakeholders.

- Mentor junior engineers on best practices for machine learning operations (MLOps) and the ethical deployment of generative technologies.

Required Skillset:

- Demonstrated expertise in building and scaling Generative AI applications using frameworks such as LangChain, LlamaIndex, or AutoGPT, with a deep understanding of LLM orchestration.

- Proven ability to design and implement complex machine learning pipelines, including data preprocessing, model training, and deployment in cloud environments like AWS, GCP, or Azure.

- Strong proficiency in Python and deep learning libraries such as PyTorch or TensorFlow, combined with the ability to write clean, maintainable, and production-ready code.

- Exceptional communication skills, with the ability to articulate technical AI concepts to non-technical stakeholders and influence product roadmaps.

- A collaborative mindset that thrives in a hybrid work environment, balancing independent research with active participation in cross-functional team sprints.

- A Masters or Bachelors degree in Computer Science, Artificial Intelligence, or a related quantitative field, backed by 6 - 10 years of professional experience in software engineering or data science.

- High adaptability to the rapidly evolving AI landscape, demonstrated by a history of quickly adopting new research papers, tools, and architectural patterns.