Machine Learning Engineer - LLM

About Us

Join a forward-thinking team at Tecblic, where innovation meets cutting-edge technology.

We specialize in delivering AI-driven solutions that empower businesses to thrive in the digital age.

If you're passionate about LLMs, machine learning, and pushing the boundaries of Agentic AI, we'd love to have you on board.

Key Responsibilities

• Research and Development: Research, design, and fine-tune machine learning models, with a focus on Large Language Models (LLMs) and Agentic AI systems.
• Model Optimization: Fine-tune and optimize pre-trained LLMs for domain-specific use cases, ensuring scalability and performance.
• Integration: Collaborate with software engineers and product teams to integrate AI models into customer-facing applications and platforms.
• Data Engineering: Perform data preprocessing, pipeline creation, feature engineering, and exploratory data analysis (EDA) to prepare datasets for training and evaluation.
• Production Deployment: Design and implement robust model deployment pipelines, including monitoring and managing model performance in production.
• Experimentation: Prototype innovative solutions leveraging cutting-edge techniques like reinforcement learning, few-shot learning, and generative AI.
• Technical Mentorship: Mentor junior team members on best practices in machine learning and software engineering.
  
  

Requirements

Core Technical Skills :

• Proficiency in Python for machine learning and data science tasks.
• Expertise in ML frameworks and libraries like PyTorch, TensorFlow, Hugging Face, Scikit-learn, or similar.
• Solid understanding of Large Language Models (LLMs) such as GPT, T5, BERT, or Bloom, including fine-tuning techniques.
• Experience working on NLP tasks such as text classification, entity recognition, summarization, or question answering.
• Knowledge of deep learning architectures, such as transformers, RNNs, and CNNs.
• Strong skills in data manipulation using tools like Pandas, NumPy, and SQL.
• Familiarity with cloud services like AWS, GCP, or Azure, and experience deploying ML models using tools like Docker, Kubernetes, or serverless functions.
  
  

Additional Skills (Good To Have)

• Exposure to Agentic AI (e., autonomous agents, decision-making systems) and practical implementation.
• Understanding of MLOps tools (e.
  
  

, MLflow, Kubeflow) to streamline workflows and ensure production reliability.

• Experience with generative AI models (GANs, VAEs) and reinforcement learning techniques.
• Hands-on experience in prompt engineering and few-shot/fine-tuned approaches for LLMs.
• Familiarity with vector databases like Pinecone, Weaviate, or FAISS for efficient model retrieval.
• Version control (Git) and familiarity with collaborative development practices.
  
  

General Skills

• Strong analytical and mathematical background, including proficiency in linear algebra, statistics, and probability.
• Solid understanding of algorithms and data structures to solve complex ML problems.
• Ability to handle and process large datasets using distributed frameworks like Apache Spark or Dask (optional but useful).
  
  

Soft Skills

• Excellent problem-solving and critical-thinking abilities.
• Strong communication and collaboration skills to work with cross-functional teams.
• Self-motivated, with a continuous learning mindset to keep up with emerging technologies
  
  

(ref:hirist.tech)