AI/ML Engineer

Job Summary 

We are seeking a talented AI/ML Engineer to design, develop, and deploy intelligent systems, including, but not limited to, recommendation engines, conversational AI agents, or video-chatting avatars. The ideal candidate will have hands-on experience with LLMs, AI agents, and fundamental machine learning techniques, contributing to end-to-end AI solution development in a collaborative, fast-paced environment. 

Key Responsibilities 

• Model Development: Design and implement machine learning models and AI systems for recommendation systems, chatbots, and AI-driven avatars using frameworks like TensorFlow, PyTorch, or Hugging Face. 
• NLP & Conversational AI: Build and optimize conversational AI agents using state-of-the-art NLP techniques (e.g., transformers, BERT, GPT) and frameworks like spaCy or Rasa. 
• Algorithm Implementation: Apply and optimize fundamental machine learning algorithms (e.g., regression, clustering, classification, decision trees, neural networks) for various use cases. 
• End-to-End Ownership: Take ownership of the AI project lifecycle, from data preprocessing and feature engineering to model training, evaluation, and deployment. 
• System Integration: Integrate AI models into production environments, ensuring scalability, reliability, and performance under real-world conditions. 
• Continuous Learning: Implement systems for continuous model improvement, leveraging supervised, unsupervised, and reinforcement learning techniques. 
• Evaluation & Testing: Develop robust testing and evaluation pipelines to measure model performance (e.g., accuracy, precision, recall, response time) and drive iterative improvements. 
• Collaboration: Work closely with cross-functional teams, including project managers, data scientists, and software engineers, to deliver data-driven solutions. 
• Technical Documentation: Maintain thorough documentation of models, pipelines, and architectural decisions to ensure clarity and reproducibility. 

Qualifications 

• Education: Minimum Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. Master’s degree preferred. 
• Experience: Minimum 2 years of hands-on experience in AI/ML engineering, with a focus on recommendation systems, chatbots, or LLMs in real-world applications. 

Technical Skills: 

• Programming: Advanced proficiency in Python, with experience in data science libraries (e.g., NumPy, Pandas, scikit-learn) and ML frameworks (e.g., TensorFlow, PyTorch, Keras, Hugging Face). 
• Machine Learning: Strong understanding of fundamental ML techniques, including: 
• Supervised learning (e.g., linear/logistic regression, SVM, random forests). 
• Unsupervised learning (e.g., k-means clustering, PCA, autoencoders). 
• Deep learning (e.g., CNNs, RNNs, transformers). 
• Natural Language Processing (NLP) (e.g., tokenization, embeddings, sentiment analysis). 
• NLP Expertise: Experience with transformer models (e.g., BERT, GPT) and NLP frameworks like spaCy, Rasa. 
• LLMs & AI Agents: Hands-on experience with Large Language Models and AI agent frameworks for conversational or autonomous systems. 
• MLOps: Familiarity with end-to-end ML pipelines, including model training, deployment, and monitoring using tools like MLflow, Kubeflow, or Docker. 
• Cloud Platforms: Experience with cloud-based AI services (e.g., AWS SageMaker, Google Cloud AI, Azure ML) for scalable model deployment. 
• Databases: Proficiency in SQL/NoSQL databases for data retrieval and preprocessing. 
• Software Engineering: Knowledge of software engineering practices, including version control (Git), CI/CD pipelines, and containerization (Docker). 
• Foundational Knowledge: Strong grasp of probability, statistics, and linear algebra for model development and evaluation. 

Preferred Technical Skills: 

• Experience with LangChain and LangGraph for building LLM-powered applications and agentic workflows. 
• Familiarity with Agentic AI frameworks and methodologies for developing autonomous, reasoning-based AI agents. 
• Familiarity with reinforcement learning frameworks or generative AI techniques. 
• Experience with web frameworks (e.g., Flask, FastAPI) for deploying AI models. 
• Knowledge of big data technologies (e.g., Apache Spark, Kafka) or data visualization tools (e.g., Tableau, Power BI). 
• Experience with vector databases (e.g., Pinecone, ChromaDB, Weaviate) for embedding search and semantic retrieval 
• Familiarity with RAG pipelines, including document chunking, embedding, and retrieval for context-aware LLM output. 
• Awareness of AI fairness, bias mitigation, and responsible deployment practices. 

Soft Skills:  

• Strong analytical and problem-solving skills, with an ability to tackle complex challenges. 
• Excellent communication and collaboration skills for working with cross-functional teams. 
• Proactive, self-driven mindset with a passion for continuous learning and innovation. 
• Detail-oriented with strong documentation skills. 
• Comfortable working in a fast-paced, agile environment (e.g., Scrum/Kanban). 
• Passion for applying AI to real-world domains such as healthcare, education, legal field, behavioral engagement, or social platforms. 

Working Days:

Monday-Friday

Benefits:

• Competitive Salary
• Social Security Fund
• Festival Allowances
• Medical Treatment, Health and Maternity Protection Scheme
• Accident and Disability Protection Scheme
• Dependent Family Protection Scheme
• Old Age Protection Scheme
• Paid Holidays
• International Work Environment
• Outing
• Loan Schemes
• Employee Referral Bonus
• Paid Certification
• Subsidized Lunch
• Annual Employee Recognition and Reward
• Annual Performance Review
• 5 Days Working Environment