Machine Learning Engineer

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.