Machine Learning Engineer – LLM and Agentic AI

Job Description:

Machine Learning Engineer – LLM and Agentic AI

Location:

Ahmedabad

Experience:

6 to 7 years

Employment Type:

Full-Time

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.
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Requirements
Core Technical

Skills:

• Proficiency in Python for machine learning and data science tasks.

• Expertise in ML frameworks and libraries like PyTorch, Tensor Flow, 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, Num Py, 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