Machine Learning Engineer

Company Description

Webappclouds specializes in creating cutting-edge mobile and web applications that bring ideas to life. Our talented team of software developers and designers ensures seamless, user-friendly apps that connect businesses with millions of users on iOS and Android platforms. In addition to app development, we provide services like website design, e-commerce solutions, and digital marketing. Our team is committed to innovation, creativity, and delivering the highest level of customer satisfaction. We work tirelessly to help businesses thrive through tailored digital solutions.

Role Description

We are seeking a Junior Machine Learning Engineer for a full-time, on-site position located in Hyderabad. The engineer will be responsible for designing, developing, and deploying machine learning models. Daily tasks include building algorithms, analyzing large datasets, implementing pattern recognition techniques, and optimizing systems for performance. Collaboration with cross-functional teams to solve complex challenges is an integral part of this role.

Qualifications

Strong understanding of Machine Learning concepts, including Neural Networks and Pattern Recognition

In-depth knowledge of Computer Science and proficiency in algorithms

Background in Statistics and data analysis techniques

Proficiency in programming languages commonly used for machine learning (e.g., Python, R, or Java)

Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, or Scikit-learn)

Strong problem-solving and critical-thinking skills

Bachelor's or Master's degree in Computer Science, Data Science, or a related field

Familiarity with big data tools and platforms (e.g., Hadoop, Spark) is a plus

LLMs :

LLM Application : Practical experience applying Large Language Models (LLMs) to solve business problems.

Architectural Pattern : Deep knowledge and hands-on implementation of RAG (Retrieval-Augmented Generation) systems.

Model Adaptation : Proficiency in Fine-tuning techniques like LoRA and Transfer Learning.

NLP Tasks : Core experience with Text Classification, Prediction, and Sequence-to-Sequence Modeling.

Tools : Hands-on with Hugging Face Transformers, LangChain, or LlamaIndex.

Deep Learning (DL) Architecture :

Framework Mastery : Strong proficiency in PyTorch (preferred) and / or TensorFlow / Keras.

Model Understanding : Deep theoretical and practical knowledge of core architectures, especially Transformer Networks (Attention), CNNs, and RNNs.

Math Foundation : Solid grasp of Linear Algebra, Calculus, and Statistics.

MLOps and Operations :

Productionisation : Ability to build and maintain scalable, reliable ML / GenAI deployment pipelines.

Cloud Platforms : Experience with ML services on at least one major cloud provider (AWS / GCP / Azure).

Model Serving : Hands-on deployment via REST APIs (Flask / FastAPI).

MLOps Practice : Understanding of model monitoring, versioning, and automated retraining (CI / CD / CT).

Foundational Code and Data :

Programming : Expert Python with OOPS, focusing on modular and scalable code.

Libraries : Proficiency with NumPy, Pandas.

Software Practice : Essential use of Git and software engineering best practices.

Data Handling :

Experience with SQL / NoSQL databases and data labelling, preprocessing for ML / DL models.