Machine Learning Engineer
2 weeks ago
Key Responsibilities:
- Design and implement modular, reusable AI agents capable of autonomous decision-making using LLMs, APIs, and tools like LangChain, AutoGen, or Semantic Kernel.
- Engineer prompt strategies for task-specific agent workflows (e.g., document classification, summarization, labeling, sentiment detection).
- Integrate ML models (NLP, CV, RL) into agent behavior pipelines to support inference, learning, and feedback loops.
- Contribute to multi-agent orchestration logic including task delegation, tool selection, message passing, and memory/state management.
- Collaborate with MLOps, data engineering, and product teams to deploy agents at scale in production environments.
- Develop and maintain agent evaluations, unit tests, and automated quality checks for reliability and interpretability.
- Monitor and refine agent performance using logging, observability tools, and feedback signals.
Required Qualifications:
- Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field.
- 3+ years of experience in developing AI/ML systems; 1+ year in agent-based architectures or LLM-enabled automation.
- Proficiency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn).
- Experience with LLM frameworks (LangChain, AutoGen, OpenAI, Anthropic, Hugging Face Transformers).
- Strong grasp of NLP, prompt engineering, reinforcement learning, and decision systems.
- Knowledge of cloud environments (AWS, Azure, GCP) and CI/CD for AI systems.
Preferred Skills:
- Familiarity with multi-agent frameworks and agent orchestration design patterns.
- Experience in building autonomous AI applications for data governance, annotation, or knowledge extraction.
- Background in human-in-the-loop systems, active learning, or interactive AI workflows.
- Understanding of vector databases (e.g., FAISS, Pinecone) and semantic search.
Why Join Us:
- Work at the forefront of AI orchestration and intelligent agents.
- Collaborate with a high-performing team driving innovation in enterprise AI platforms.
- Opportunity to shape the future of AI-based automation in real-world domains like healthcare, finance, and unstructured data.
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