Senior AI/ML Engineer

• XXgn, develop, and deploy intent classification and intent detection models using LLMs and traditional NLP methods.
• Build and optimize Natural Language Generation (NLG) pipelines for chatbot responses, summarization, content creation, or knowledge grounding.
• Architect and implement LangChain and LangGraph-based applications for LLM-driven workflows (e.g., autonomous agents, RAG systems).
• Develop scalable machine learning pipelines using the AWS tech stack (e.g., Sagemaker, LamClienta, Bedrock, Step Functions, DynamoDB, Athena).
• Integrate and fine-tune foundation models via AWS Bedrock, including Amazon Titan, Anthropic Claude, or Meta Llama.
• Collaborate closely with product managers, ML researchers, and backend engineers to translate business requirements into robust AI solutions.
• Lead experimentation efforts, conduct A/B testing, and ensure continuous evaluation of deployed ML models.
• Candidatetor junior ML engineers and contribute to best practices in MLOps, model governance, and responsible AI.

• Total 4 to 5 + years of experience in machine learning, with a focus on NLP and Generative AI.
• Strong experience building and deploying intent detection, text classification, sequence tagging, and entity recognition models.
• Proficient in LangChain, LangGraph, vector databases (e.g., FAISS, Pinecone), and orchestration of LLM workflows.
• Deep knowledge of AWS Bedrock, Amazon SageMaker, Lambda, DynamoDB, Step Functions, etc.
• Experience working with open-source LLMs (LLaMA, Mistral, Falcon) or commercial APIs (Claude, GPT-4, etc.).
• Proficient in Python, with a solid grasp of ML frameworks such as PyTorch, HuggingFace Transformers, and scikit-learn.
• Strong understanding of MLOps practices, including model versioning, CI/CD for ML, monitoring, and auto-scaling.
• Bachelor's or Master's in Computer Science, Data Science, or a related field.

• Experience integrating RAG (Retrieval-Augmented Generation) systems at scale.
• Familiarity with vector search using Amazon OpenSearch, Pinecone, or Weaviate.
• Experience with streaming data processing (e.g., AWS Kinesis, Kafka).
• Contributions to open-source AI/ML or NLP projects.

Cynet Systems