Senior AI/FDE Engineer

Taleo; Ernakulam; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Taleo is seeking a seasoned AI/ML engineer to implement GenAI capabilities across client-facing suites in a forward deployed model. You will build RAG and GraphRAG solutions, develop embeddings and vector-store pipelines, and integrate AI features with enterprise systems and APIs.

The role involves collaborating with product, business, and engineering teams, validating outputs, and supporting field deployments with field configuration and demos. Strong Python and backend experience are essential.

Qualifications

• 5+ years hands-on AI/ML/data/backend or solution engineering experience with strong Python fundamentals.
• Practical experience building GenAI/LLM apps using RAG, GraphRAG, embeddings, and vector stores.
• Experience integrating AI features with enterprise systems, APIs, data pipelines, or orchestration layers.

Responsibilities

• Develop and configure AI-enabled features across client-facing capability suites.
• Implement RAG, GraphRAG, prompt workflows, agent routing, and retrieval pipeline integrations.
• Translate field requirements into deployable technical solutions with product, business, data, and engineering teams.
• Build dynamic query-generation and graph-aware retrieval capabilities for knowledge-graph-backed use cases.
• Integrate AI capabilities with backend APIs, orchestration services, vector stores, and data sources.
• Validate outputs against datasets, acceptance criteria, grounding rules, and quality expectations.
• Support field deployment, configuration, troubleshooting, and production-readiness activities.
• Document solution behavior, deployment assumptions, known limitations, and upgrade paths.

Skills

Python

GenAI

RAG

GraphRAG

Embeddings

Vector stores

APIs

Tools

LangChain

LangGraph

Job description

Role summary

Implements AI and Forward Deployed Engineering capabilities across client-facing feature suites, combining hands-on GenAI engineering with solution deployment, integration, configuration, and field feedback loops. The role focuses on building and adapting RAG, GraphRAG, agentic workflows, orchestration integrations, and data-connected AI features so they work reliably in real enterprise environments.

Key responsibilities

• Develop and configure AI-enabled features across assigned client-facing capability suites
• Implement RAG, GraphRAG, prompt workflows, agent routing, and retrieval pipeline integrations
• Work directly with product, business, data, and engineering stakeholders to translate field requirements into deployable technical solutions
• Build dynamic query-generation and graph-aware retrieval capabilities for knowledge-graph-backed use cases
• Integrate AI capabilities with backend APIs, orchestration services, vector stores, enterprise data sources, and platform services
• Validate outputs against golden datasets, acceptance criteria, grounding rules, and enterprise quality expectations
• Support field deployment, configuration, troubleshooting, demos, UAT feedback, and production-readiness activities
• Document solution behavior, deployment assumptions, known limitations, and upgrade paths for future releases

Required skills & experience

• 5+ years hands-on AI, ML, data, backend, or solution engineering experience, with strong Python fundamentals
• Practical experience building GenAI / LLM applications using RAG, GraphRAG, prompt engineering, embeddings, and vector stores
• Experience integrating AI features with enterprise systems, APIs, data pipelines, orchestration layers, or workflow platforms
• Comfortable working in a Forward Deployed Engineering model, including client-facing discovery, rapid prototyping, field configuration, issue triage, and stakeholder demos
• Familiarity with agent frameworks such as LangGraph, LangChain, MCP-based integrations, or comparable orchestration patterns
• Ability to validate AI outputs using golden datasets, regression checks, grounding criteria, and measurable acceptance criteria
• Strong communication skills with the ability to explain technical trade-offs, implementation constraints, risks, and field observations to technical and business stakeholders

Preferred qualifications

Experience delivering AI solutions in regulated, enterprise, healthcare, pharma, manufacturing, or data-governed environments