Specialist I - Software Engineering (AI/ML Architect) @ UST

Technical Skills:

Advanced proficiency in Python.

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

Must Have : Microservices LLM, Python, FastAPI, Vector DB(Qdrant, Chromadb, stc), RAG, MLOps & Deployment, Cloud, Agentic AI Framework, Kubernetes, Architecture & Design

Secondary Skills - Data science, ML and NLP