JOB RESPONSIBILITY

• Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization.

• Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models.

• Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements.

• Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments.

• Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment.

• Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. 

• Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. 

• Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. 

• Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling.

• Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function.

• Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. 

QUALIFICATION Minimum education: Bachelor’s degree in any Engineering Stream Specialized training, certifications, and/or other special requirements: Nice to have Preferred education: Computer Science/Engineering. 

EXPERIENCE Minimum relevant experience - 4+ years in AI Engineering SKILLS AND COMPETENCIES

Technical Skills: • Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow)

• Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques

• Experience with big data processing using Spark for large-scale data analytics

• Version control and experiment tracking using Git and MLflow 

• Software Engineering & Development: 

Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.

• LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.

• MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.

• LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.

• General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. 

• Experience in creating LLD for the provided architecture. 

• Experience working in microservices based architecture.

Domain Expertise: • Strong mathematical foundation in statistics, probability, linear algebra, and optimization 

• Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation 

• Expertise in feature engineering, embedding optimization, and dimensionality reduction

• Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing

• Experience with RAG systems, vector databases, and semantic search implementation 

• Proficiency in LLM optimization techniques including quantization and knowledge distillation

• Understanding of MLOps practices for model deployment and monitoring

Professional Competencies: • Strong analytical thinking with ability to solve complex ML challenges 

• Excellent communication skills for presenting technical findings to diverse audiences

• Experience translating business requirements into data science solutions

• Project management skills for coordinating ML experiments and deployments 

• Strong collaboration abilities for working with cross-functional teams 

• Dedication to staying current with latest ML research and best practices

• Ability to mentor and share knowledge with team members