Senior Engineering Consultant-Cloud & AI

verizon

Hyderabad 6 Years Exp Posted 41d ago

You'll need to have:

Bachelor's degree or four or more years of hands-on work experience.
Six or more years of relevant experience.
Experience with a strong Python focus — clean, production-grade, testable code.
Deep, hands-on experience with the Python ecosystem for AI/ML and data workflows (LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks.
Demonstrated experience building and deploying LLM-powered agents or applications in a production environment.
Strong understanding of LLM concepts: prompt engineering, RAG, tool/function calling, context windows, structured outputs, and agent memory patterns.
Experience integrating AI systems with relational databases (Postgres or equivalent) and REST APIs.
Solid understanding of software engineering fundamentals: version control (Git), code review, testing, and documentation practices.
Ability to work US Central Standard Time (CST) business hours (8:00 AM to 5:00 PM CT), which corresponds to 6:30 PM to 3:30 AM Indian Standard Time.

Even better if you have:

Hands-on experience with AI agent frameworks and developer tools — such as Claude Code, OpenAI Assistants, or similar agentic platforms — including building custom tooling on top of them.
Experience with MLOps practices: model versioning, pipeline monitoring, experiment tracking, and production observability for AI systems.
Familiarity with DevOps tooling — Ansible, Jenkins, GitLab CI — and comfort working alongside infrastructure automation engineers.
Linux server experience and Shell scripting skills for deploying and debugging AI applications in server environments.
Experience with containerization (Docker, Kubernetes) for deploying AI workloads.
Exposure to telecommunications, network operations, or infrastructure automation use cases — experience applying AI to ops problems like anomaly detection, log analysis, or failure prediction.
Familiarity with vector databases (pgvector, Pinecone, Weaviate, or similar) for semantic search and RAG pipelines.
- Experience with streaming or event-driven architectures (Kafka, Redis) for real-time AI agent integrations.