AI/ML Engineer

Role overview Join as an AI/ML Engineer focused on designing and shipping production-grade artificial intelligence systems for client engagements. The role spans the full ML lifecycle—from advisory and architecture through hands-on implementation and ongoing operations—working with large language models, retrieval pipelines, and real-world data at scale.

Responsibilities - Consult with client stakeholders to evaluate data readiness and recommend the right ML approach, ranging from prompt engineering to fine-tuning and full custom architectures. - Build and optimize retrieval-augmented generation pipelines, multi-agent systems, and semantic search components. - Design data ingestion, chunking, and embedding workflows that feed downstream AI applications. - Manage model selection, parameter-efficient fine-tuning (e.g., PEFT, LoRA), and deployment configuration, then iterate based on evaluation metrics. - Maintain production reliability through monitoring, scalability tuning, and proactive cost optimization. - Hand off and collaborate with client-side technical leadership such as CTOs and Lead Data Scientists.

Requirements - 5+ years of professional experience in Machine Learning or Data Engineering with deep Python proficiency. - Production experience with PyTorch or TensorFlow, plus strong familiarity with LLM ecosystems such as OpenAI, Hugging Face Transformers, and LangChain. - Hands-on experience building RAG pipelines and managing vector databases (e.g., Pinecone, Milvus, Weaviate, Qdrant, pgvector). - Working knowledge of major cloud platforms (AWS, GCP, or Azure). - Familiarity with data engineering tooling such as Apache Airflow and Spark, along with MLOps and collaborative development practices.

Nice to have - Experience deploying and fine-tuning open-source models such as Llama 3 or Mistral. - Familiarity with managed cloud AI services including AWS SageMaker, Vertex AI, or Azure AI.