Senior AI/FDE Engineer
Milestone Technologies (company site, opens in a new tab)5+ yrsHyderabadOn-siteDirect career pageEst. 18–32 LPA
Posted on 17 Sept
Reach people at Milestone Technologies
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Milestone Technologies you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Build and deploy AI-enabled features such as RAG, GraphRAG, prompt workflows, and agent routing within client-facing products.
- Integrate AI capabilities with backend APIs, orchestration services, and enterprise data sources ensuring reliability in enterprise environments.
- Support field activities including troubleshooting, demos, UAT feedback, production readiness, and documentation of solution behavior and limitations.
Minimum Requirements
- 5+ years of hands-on experience in AI, ML, data, backend, or solution engineering with strong Python skills.
- Proven experience developing GenAI/LLM applications using RAG, GraphRAG, prompt engineering, embeddings, and vector stores.
- Experience in integrating AI features with enterprise systems, APIs, data pipelines, orchestration layers, or workflow platforms.
- Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced in Forward Deployed Engineering including client-facing discovery, rapid prototyping, configuration, and stakeholder communication.
- Familiarity with agent frameworks like LangGraph, LangChain, MCP-based integrations, or similar orchestration patterns.
- Ability to validate AI outputs against golden datasets, grounding criteria, and measurable acceptance criteria, indicating a detail-oriented and quality-focused approach.