AI Engineer

• Design and develop GenAI models and applications from concept to production, including model selection, fine-tuning, and optimization for specific business use cases.
• Build end-to-end AI solutions that integrate seamlessly with existing enterprise systems and workflows.
• Implement sophisticated prompting strategies and language engineering techniques to maximize model performance and reliability.
• Create functional demonstration interfaces and prototypes using tools like Gradio, Streamlit, or custom web applications.
• Stay current with the latest developments in GenAI, including models from OpenAI, Google, Anthropic, and open-weight alternatives.

• Build robust, scalable APIs and microservices that serve AI models in production environments.
• Develop containerized applications using Docker and orchestration platforms for reliable deployment.
• Create and maintain clean, well-documented code that follows best practices for enterprise software development.
• Implement proper error handling, logging, and monitoring for AI applications.
• Collaborate with cross-functional teams using agile development methodologies.

• Design and implement robust data pipelines for preparation, cleaning, and integration of diverse data sources.
• Handle enterprise data challenges including Excel files, PowerPoint presentations, and Office 365 integrations.
• Build ETL processes that ensure data quality and consistency for AI model training and inference.
• Implement data processing solutions that scale efficiently with growing data volumes.
• Develop data validation and monitoring systems to maintain pipeline reliability.

• Integrate AI solutions with existing business systems, databases, and enterprise applications.
• Navigate complex enterprise environments and work with legacy systems and data formats.
• Implement security best practices and ensure compliance with enterprise governance requirements.
• Manage model lifecycle including version control, A/B testing, and performance monitoring.
• Build solutions that handle enterprise-scale data processing and user loads.

• Experiment with emerging GenAI technologies, frameworks, and methodologies
• Conduct applied research to solve novel business problems using state-of-the-art AI techniques.
• Evaluate and benchmark different AI models and approaches for specific use cases.
• Contribute to the lab's knowledge base and share learnings across the team
• Participate in the broader AI community through conferences, publications, or open-source contributions.

• 5+ years of software engineering experience with strong proficiency in Python and modern development practices.
• Minimum 2 years of hands-on experience with LLMs, RAG systems, and generative AI applications.
• Proven track record of building and deploying production AI/ML systems in enterprise environments.
• Experience with API development, microservices architecture, and cloud platforms.
• Background in data engineering, ETL processes, and working with enterprise data systems.

• GenAI Frameworks: OpenAI API, Anthropic Claude, Google Gemini, Hugging Face transformers, LangChain, LlamaIndex.
• Language Engineering: Advanced prompting techniques, chain-of-thought reasoning, RAG implementation, fine-tuning strategies.
• Software Development: Python, FastAPI/Flask, RESTful APIs, microservices architecture, containerization (Docker).
• Data Engineering: ETL pipelines, data processing frameworks (pandas, dask), database systems (SQL/NoSQL), data quality management.
• Enterprise Integration: Office 365 APIs, SharePoint, Azure/AWS services, enterprise authentication systems.
• MLOps: Model versioning, CI/CD for ML, monitoring and observability, A/B testing frameworks.
• NLP Fundamentals: Text processing, embedding models, semantic search, document parsing and analysis.
• Cloud Platforms: Azure, AWS, or GCP with experience in managed AI services
• Development Tools: Git, JIRA, agile methodologies, code review processes.
• Data Formats: JSON, XML, Excel processing, document parsing, unstructured data handling.

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