Primary Skill Set Python, FastAPI, Microservices , SQL/No SQL, MLOps & Deployment, Kubernetes, Obeservability LLM, , Vector DB(Qdrant, Chromadb, stc), RAG,

Description:

• 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.

• Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management.

• Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows.

• 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. • Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. • 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.

• SKILLS AND COMPETENCIES Technical Skills:

• Advanced proficiency in Python

• 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: Knowledge 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: Experience 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.

• Experience working in Observability. Professional Competencies:

• Strong analytical thinking with ability to solve complex challenges

• Excellent communication skills for presenting technical findings to diverse audiences

• Experience translating business requirements into data science solutions • 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