Key Responsibilities:
• Collaborate with data scientists and product managers to understand business needs and translate them into technical requirements for machine learning solutions.
• Design, develop, and implement machine learning models, utilizing a variety of algorithms and techniques including [mention specific examples relevant to the company's focus].
• Build and manage data pipelines for training and production models, ensuring data quality and efficiency.
• Train, evaluate, and optimize machine learning models, focusing on performance, fairness, and interpretability.
• Deploy and monitor models in production environments, ensuring scalability, reliability, and security.
• Continuously improve existing models and develop new ones to address evolving business needs and technological advancements.
• Document all stages of the machine learning workflow, ensuring reproducibility and knowledge sharing within the team.
• Collaborate with other engineers, developers, and researchers to build and integrate machine learning solutions into various systems and applications.
• Stay informed about the latest developments in the field of machine learning and contribute to the continuous learning and growth of the team.

Required Skills and Qualifications:
• Master's degree in Computer Science, Statistics, or any Computational intensive engineering fields.
• Strong experience in machine learning algorithms and Architectures
• Strong Experience in Deep Learning approaches and ability to compose novel architectures based on current state of art in, e.g., Transformers and Convolutional.
• Proficiency in Algorithmic design and analysis, and high degree of comfort in using formal mathematical methods.
• Experience working with Analytic data and ML tools and frameworks.
• Excellent communication and collaboration skills
• Ability to work independently and as part of a team
• Passion for machine learning and its potential to solve real-world problems

Desired Technical Experience Qualifications:
• ML/AI Tools, Libraries, and/or Systems
o Languages: Julia, Python, RUST,GOLang etc.
o Architectures: Convolutional, Attention Transformers, Variational autoencoders, Reinforcement Learning, Generative Adversarial Networks, Large Language Models(Transfomers)
o Inferencing: Probablistic graphical models, Bayesian methods,
o Optimization : Batch/Stochastic Gradient Descent, Hogwild!, Adagrad
o ML Frameworks: PyTorch, TensorFlow, Flux.jl etc
o Data Analysis : Modin, Ray, Pandas, DataFrames.jl, Numpy, Dask
o Graph Modelling : Neo4J, Networkx etc.
o Vector Storage : Milvus, Pinecone, Apache Solr, Weaviate
o Search Frameworks: Haystack, Jina.AI

Experience and Integration
o GUI prototyping frameworks:Streamlit, Gradio
o Web App Frameworks : React, Gatsby
o API Frameworks: FastAPI
•Backend
o Docker Containerization, Compose, o Cache and No-SQL DBS : Redis, Neo4j, MongoDB, TerminusDB
o Distributed Data Processing ,Training , and Inferencing: Seldon Core, KubeFlow, CollossalAI
• Scaling Heterogeneous Computing frameworks: CUDA, ONNX, GLUE
o Cloud : AWS, Azure, Google Cloud Platform
• Experience in Dev, ML, & Model OPS
• Familiarity with ethical considerations in AI development
• Strong written and verbal communication skills
• Ability to manage complex projects and meet deadlines