Full-Time AI Engineer

The AI Engineer will play a pivotal role in designing, developing, and deploying advanced AI solutions, with a strong focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Multi-Modal Generative AI. Working closely with data scientists, product managers, engineers, and other stakeholders, you will build scalable, production-grade AI solutions that address customer needs and enable operational excellence.

Key Responsibilities:

1. AI Solution Development: Design, develop, and optimize AI algorithms and models tailored to business needs, ensuring scalability and performance. Develop APIs and services to make AI functionalities accessible across the organization.
2. Data Preparation & Processing: Preprocess and prepare diverse data types (PDFs, Word documents, Excel files, HTML, audio, video, and databases) for machine learning models. Ensure data quality, security, and readiness for AI applications.
3. LLM Applications: Build advanced LLM applications, including Retrieval-Augmented Generation (RAG) workflows, fine-tuning, and embedding models. Implement reasoning and agent-based systems, leveraging tools like LangGraph, LangChain. Evaluate and optimize performance with techniques like RAGAS and advanced retrieval mechanisms.
4. Cloud Deployment & Operations: Deploy AI and LLM applications to cloud infrastructure (AWS preferred). Manage production-grade solutions with tools like Amazon SageMaker, implementing monitoring, scaling, and visibility tools.
5. Performance Monitoring & Troubleshooting: Track the performance of deployed AI solutions using monitoring tools and feedback mechanisms. Continuously improve solution quality by analyzing outputs and addressing identified issues.
6. Research & Innovation: Stay updated on emerging AI methodologies, frameworks, and technologies. Incorporate cutting-edge developments into existing workflows and create reusable AI frameworks.
7. Cross-Functional Collaboration: Work with stakeholders such as delivery leads, product managers, and domain experts to translate business needs into actionable AI solutions. Collaborate with IT and cloud operations teams to ensure seamless integration and scalability of AI tools.
8. Documentation & Best Practices: Maintain detailed documentation of AI models, processes, and workflows. Establish and promote best practices for AI development, deployment, and maintenance across teams.

Technical Expertise:

• Proficiency in AI techniques such as RAG, LLM fine-tuning, and embedding models.
• Advanced Python programming skills and experience with frameworks like LangGraph, LangChain.
• Expertise in handling large datasets, including vectorized datasets, and using ETL/ELT tools.
• Cloud & MLOps: Hands-on experience deploying and managing AI solutions on AWS. Familiarity with containerization technologies like Docker and MLOps practices for efficient model lifecycle management.

Problem Solving & Collaboration:

• Strong analytical and problem-solving skills for tackling AI development challenges.
• Excellent communication skills to collaborate with cross-functional teams and articulate technical concepts clearly.

Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

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