Machine Learning Engineer

About the job

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. 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.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. 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)