Black Kite Technologies - Artificial Intelligence/Machine Learning Engineer - LLM & Agentic AI

Overview

Job Description : Machine Learning Engineer - LLM and Agentic AI

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.
  
  

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.
  
  

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

• 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).
  
  

• 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)