As an Applied AI/ML Engineer, you will lead the development, deployment, and optimization of advanced machine learning and artificial intelligence solutions within Infrastructure Platforms. Utilizing your expertise in natural language processing (NLP), machine learning (ML), and AI model fine-tuning, you will train, evaluate, and integrate large and small language models (LLMs/SLMs) into a variety of applications. You will also design, build, and maintain robust web applications and APIs, ensuring seamless integration of AI-driven features across the technology stack. Your work will accelerate the adoption of generative AI and intelligent automation, supporting business objectives and enhancing operational efficiency.

Key Responsibilities:

• Research, develop, and optimize LLMs and SLMs for diverse applications and web interfaces.
• Fine-tune pre-trained models using domain-specific datasets to improve relevance and accuracy.
• Implement, evaluate, and enhance NLP pipelines for robust model performance.
• Design efficient training workflows and optimize inference for scalability and speed.
• Collaborate with cross-functional teams to integrate LLM/SLM capabilities into products and services.
• Uphold responsible AI principles, including bias mitigation and ethical AI practices.
• Monitor advancements in generative AI and recommend improvements to existing systems.
• Develop APIs and tools to facilitate model deployment and accessibility.
• Conduct rigorous testing and benchmarking of language models to ensure quality and reliability.
• Design, build, and maintain web applications using modern JavaScript frameworks and server-side technologies.
• Develop and manage RESTful APIs and databases to support AI-driven features.

Required Qualifications, skills & capabilities

• Experience with LLMs (e.g., GPT, LLaMA, Falcon) and SLMs (e.g., distilled/task-specific models).
• Proficiency in NLP techniques: tokenization, embeddings, attention mechanisms.
• Expertise in data preprocessing, augmentation, and curation for model training.
• Hands-on experience with cloud computing (AWS, GCP, Azure) and MLOps tools.
• Knowledge of vector databases, Retrieval-Augmented Generation (RAG), and prompt engineering.
• Strong coding skills in Python; familiarity with libraries such as Transformers, Hugging Face, Lang Chain.
• Excellent problem-solving skills and collaborative team orientation.
• Experience integrating AIML models/services, including data preprocessing, deployment, and intelligent feature development.
• Strong expertise in designing, building, and maintaining web applications using JavaScript frameworks (React, Angular, or Vue.js), server-side technologies (Node.js, Express, or Python/Django), RESTful API development, and database management (MongoDB, PostgreSQL, or MySQL).
• Proficient in version control (Git), containerization (Docker), CI/CD pipelines, cloud platforms (AWS, Azure, GCP), and security best practices.