LLM-Engineering Manager(9+ years) (Python + Machine Learning)

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.