AI LLM,GCP Python Eng 5 Yrs

A remote opportunity in the Enterprise AI & Cloud Platform engineering sector focused on building production-grade LLM-powered services and ML pipelines on Google Cloud. This role drives GenAI features vector search, prompt engineering, inference optimization, and MLOps for B2B and consumer-facing applications. Location: Remote (India). Primary title: Senior AI/ML Engineer - LLM & GCP (Python).
Role & Responsibilities

• Design, implement and productionize Python-based LLM services and microservices on GCP (Vertex AI) to support real-time and batch inference workloads.
• Integrate and optimize transformer models (Hugging Face) and LangChain-based workflows for retrieval-augmented generation and agentic pipelines.
• Build and maintain vector search pipelines (FAISS or managed alternatives), embedding stores, and low-latency query paths for semantic search.
• Develop robust MLOps: containerization, CI/CD, infra-as-code, autoscaling, monitoring and rollback procedures for model deployments on Kubernetes.
• Collaborate with Data Scientists and Product teams to define model evaluation, A/B testing, inference cost/perf trade-offs, and prompt engineering best practices.
• Implement secure, observable APIs and backend services (FastAPI/REST) with logging, tracing and SLIs to ensure production reliability.
  

Skills & Qualifications Must-Have

• Python
  
• Hugging Face Transformers
• LangChain
• Google Cloud Platform
• Vertex AI
• Docker
• Kubernetes
• REST APIs
  

Preferred

• FAISS
• PyTorch

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• Apache Airflow
  

Qualifications

• Approximately 5 years of hands-on experience building and shipping production ML/LLM services, with demonstrable projects or deployed systems.
• Strong practical knowledge of model inference optimization, prompt engineering, cost/performance tuning, and production monitoring.
  

Benefits & Culture Highlights

• Fully remote role (India) with flexible hours and a fast-paced, learning-focused engineering culture.
• Opportunities to work end-to-end on GenAI product features and influence MLOps best practices across teams.
• Professional development budget and access to cutting-edge ML tooling and cloud credits.
  

Skills: gcp,llm,python