Machine Learning Engineer - LLMs and Agentic

• Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
• Knowledge of AI safety, guardrails, and explainability techniques.
• Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
• Experience with CI/CD for ML (MLOps), monitoring, and observability.
• Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
• Contributions to open-source AI/ML projects or publications in applied ML research.

Originally posted on Himalayas

• Bachelor’s or Master’s degree in Computer Science, Data Science, MachineLearning, or related field.
• 3+ years of experience building and deploying ML systems.
• Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
• Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).
• Demonstrated experience with at least two of the following ecosystems:
• OpenAI GPT models (chat, assistants, fine-tuning).
• Anthropic Claude (safety-first AI for reasoning and summarization).
• Google Gemini (multimodal reasoning, enterprise-scale APIs).
• Meta LLaMA (open-source, fine-tuned models).
• Familiarity with vector databases, embeddings, and RAG pipelines.
• Ability to work with structured and unstructured data at scale.
• Knowledge of SQL and distributed data frameworks (Spark, Ray).
• Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.