Data Scientist

• 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: Bachelors 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 Role & responsibilities

Preferred candidate profile