LLM-Engineering Manager(9+ years)

4 days ago


India MindBrain Full time

Job Description Position: LLM-Engineering Manager (Python + Machine Learning) Type: Contract | Remote Experience Required: 3+ years in team management roles Location: Remote OVERALL-9+ ML/LLM-2+ About the Role: We are looking for an experienced Engineering Manager who will lead a team responsible for building, fine-tuning, and deploying large language models (LLMs) and other ML systems. You'll combine hands-on technical expertise with leadership capability: driving model development, serving production environments, and guiding your team to deliver high-impact ML solutions. Key Responsibilities: Lead, mentor and grow a team delivering ML/LLM models in production environments. Define and drive the technical roadmap for LLM and ML systems using frameworks like PyTorch, TensorFlow, Hugging Face or DeepSpeed. Oversee end-to-end model lifecycle: problem framing, data preparation, model training/fine-tuning, evaluation, deployment, monitoring & iteration. Collaborate with cross-functional teams (product, data science, infra/DevOps) to integrate LLM/ML features into applications and services. Ensure best-practices in production ML: scalability, performance, reliability, security, versioning, observability. Make architectural decisions around model serving, inference pipelines, vector indexing, semantic search, or RAG workflows (if applicable). Track metrics, report progress and help shape long-term platform evolution for ML/LLM capabilities. Required Skills & Experience: 3+ years of experience managing engineering teams focused on ML/LLM delivery. Strong hands-on technical background: Python, and one or more ML/LLM frameworks (PyTorch, TensorFlow, Hugging Face, DeepSpeed). Proven track record of delivering ML/LLM models into production environments (preferably at scale). Deep understanding of ML/LLM principles: model fine-tuning, inference, vector/search pipelines, semantic embeddings. Experience with production infrastructure around ML: serving systems, observability/monitoring, versioning, FaaS or containers. Excellent leadership and communication skills in a remote environment; ability to coordinate across teams and drive results. Nice to Have: Experience with vector databases, semantic search, retrieval-augmented generation (RAG). Familiarity with MLOps tooling, CI/CD for ML, containers (Docker/Kubernetes), cloud platforms (AWS/Azure/GCP). Exposure to multi-modal models (text+image), reinforcement learning, or other advanced AI/ML technologies.


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