AI Engineer: About the Role:
We are looking for an experienced AI Engineer to join our team and develop advanced, end-to-end AI workflows. The ideal candidate has proven, hands-on professional experience building “agentic” solutions with large language models (LLMs). You will be responsible for designing, implementing, and scaling pipelines that integrate AI for various enterprise use cases, such as extracting information from unstructured documents (PDFs, text files, etc.), transforming and summarizing data, and delivering insights to stakeholders. Key Responsibilities
- AI Workflow Development: Design, build, and maintain agentic pipelines that utilize LLMs and related AI services (e.g., Google Gemini, OpenAI API) to parse, analyze, and summarize data.
- Data Integration & Transformation: Develop data workflows that handle ingestion, cleaning, transformation, and storage of large volumes of unstructured data from various sources (e.g., PDFs, CSVs, APIs).
- Model Evaluation & Optimization: Evaluate AI models for accuracy, performance, and reliability. Continuously refine prompts, hyperparameters, and pipelines to improve model outputs and efficiency.
- Collaboration & Stakeholder Communication: Work cross-functionally with product managers, data teams, and external stakeholders to understand requirements and deliver AI-driven solutions that meet business objectives.
- Automation & Scalability: Leverage modern engineering practices to automate data processing and model deployment. Ensure systems are robust, scalable, and well-documented.
- Research & Innovation: Stay current with emerging AI trends, tools, and best practices; incorporate them to enhance existing pipelines and propose new AI initiatives.
Qualifications & Skills
- Professional Experience: 2+ years in a professional role focused on AI/ML engineering or software engineering with a heavy AI component (beyond personal or academic projects).
- Technical Expertise:
- Demonstrated experience with large language models and generative AI tools (e.g., Google Gemini, OpenAI GPT series).
- Proficiency in languages such as Python, and familiarity with popular ML frameworks/libraries (TensorFlow, PyTorch, etc.).
- Strong knowledge of data processing frameworks (e.g., Apache Spark) and relational or NoSQL databases.
- Model Lifecycle Management: Experience with the full model lifecycle—training, fine-tuning, deployment, and monitoring—in a production environment.
- Cloud & DevOps: Familiarity with cloud platforms (AWS, Azure, GCP) and understanding of containerization (Docker, Kubernetes) and CI/CD best practices.
- Problem-Solving & Communication: Excellent analytical skills and ability to translate complex AI concepts into practical business solutions. Strong written and verbal communication skills.
- Education: Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or a related field (or equivalent professional experience).