Forward Deployed Engineer - LLM/Gen AI

Evnek Technologies

IndiaremotePosted 15 days ago

Skill Required

Machine-Learning-EngineerAI-EngineerLLM-EngineerGenerative-AI-EngineerData-EngineerForward-Deployed-AI-EngineerForward-Deployed-AI-EngineeringForward-Deployed-ML-EngineerForward-Deployed-EngineerForward-Deployed-EngineeringSenior-Forward-Deployed-EngineerForward-Deployed-Software-EngineerAI EngineerGenerative AIAIObservabilityEngineeringDatabricksSnowflakesecurityEmbedded CdesignAzureCI/CDCloudDesign PatternsMachine LearningRAGAWSGCPandCICDFulltime

Key highlights

Level Required: 5-10 years
Location: Bengaluru (Remote)
Notice period: Immediate or max 15 days preferred
Notable requirement: 6-10 years in AI/data product delivery, technical consulting, or engineering

Role overview

Requirements

Experience: 5-10yrs
Solid understanding of data platforms — data lakes, pipelines, transformation, and how data flows from source to AI model
Working knowledge of Generative AI, LLMs, and RAG pipelines — enough to scope deliverables and review outputs critically
Ability to read and understand data models, architecture diagrams, and API specs — you don't write production code but understand what's being built
Cloud platforms — AWS and/or Azure — data and AI services at a conceptual and delivery level
Familiarity with Snowflake, Databricks, or equivalent — enough for informed conversations with data engineers
Experience using AI platforms and tools to rapidly prototype and demonstrate solutions to clients
6–10 years in AI/data product delivery, technical consulting, or engineering — not pure product management
Proven ability to run discovery independently — without a senior consultant holding your hand
Strong client-facing presence — comfortable presenting to senior business and technology stakeholders
Experience writing SOWs, project proposals, or technical specifications that get signed off by clients
Track record of owning delivery outcomes — accountable for the result, not just managing tasks
Ability to manage multiple workstreams simultaneously in a fast-paced pod environment
Cloud Architecture (AWS/Azure/GCP)
AI/ML, Generative AI, and Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG) and AI Agents
API Design and Enterprise Integrations
Data Pipelines, Data Lakehouse, and Data Mesh
MLOps, CI/CD, Model Registry, and Model Observability
Security, Governance, and Cost Optimization