Senior AI Engineer — GenAI Solutions

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Senior AI Engineer — GenAI Solutions

Viamagus Technologies Pvt. Ltd.

Hyderabad

4-7 years

Today

$33.7K–53.0K/yr

Full-time

Onsite

Skills Required

LLM

RAG

Gen AI

LangChain

Embeddings

Vector Database

retrieval

LLM observability

multimodal applications

LangGraph

FastAPI

Python

Pinecone

Weaviate

pgvector

Description

Senior AI Engineer role focused on building production-grade GenAI solutions and client-facing solutioning. This is a founding role for a new AI Consulting & Engineering practice serving enterprise and Fortune 500 clients.

Company: Viamagus Technologies Pvt. Ltd.

Role: Senior AI Engineer — GenAI Solutions

Location: Hyderabad, India

Experience

• 4–7 years of software engineering experience
• At least one real production LLM-powered feature shipped
• Hands-on experience with production GenAI, RAG, and agentic systems
• Fluent written and spoken English
  
  

Responsibilities

• Design and build production GenAI applications including LLM copilots, RAG pipelines, agentic workflows, and AI-native UX
• Engineer retrieval end to end, including chunking, embeddings, vector stores, retrieval, re-ranking, and hybrid search
• Build agents and multi-step workflows with LangGraph and LangChain
• Integrate MCP servers and agent SDKs for tool access
• Augment enterprise platforms with predictive insight, intelligent automation, and conversational UX
• Deploy solutions on AWS Bedrock, Azure AI Foundry, or GCP Vertex AI with CI/CD, observability, and guardrails
• Build evaluation harnesses and wire up LLM observability
• Handle responsible-AI basics including PII handling, guardrails, and audit trails
• Run client discovery workshops and translate business problems into scoped, ROI-backed AI use cases
• Demo working software to technical and business stakeholders and defend design decisions and tradeoffs
• Contribute to reusable accelerators
• Mentor junior engineers as the practice grows
  
  

Additional Responsibilities

• Work in a roughly 70% hands-on engineering and 30% client-facing solutioning split
• Take AI use cases from discovery workshop to deployed, monitored production system
• Build AI solutions for enterprise and Fortune 500 clients, not disconnected proofs of concept
• Help shape the architecture, standards, and culture of a new AI practice
• Work across a modern 2026 AI stack
• Ensure every engagement is tied to a business KPI
  
  

Nice To Have

• Experience adding AI to enterprise platforms such as Clarity PPM, Medallia, ServiceNow, or Salesforce Einstein
• Deeper MLOps experience with MLflow, Weights & Biases, model monitoring, or serverless GPU
• Data and RAG infrastructure experience with ETL/ELT, CDC, orchestration, streaming, knowledge graphs, Databricks, or Snowflake
• Fine-tuning and multimodal experience including LoRA, distillation, vision, voice, or multimodal applications
• Industry domain depth in financial services, insurance, healthcare, or retail
• AI governance awareness including NIST AI RMF and EU AI Act
• Relevant cloud AI/ML certifications
  
  

More Skills

LLM-powered features, Prompt design, Structured outputs, Tool/function calling, Streaming, Chunking, Qdrant, Re-ranking, Hybrid search, LlamaIndex, MCP, Agent SDKs, TypeScript, React, Next.js, AWS Bedrock, Azure AI Foundry, GCP Vertex AI, Docker, CI/CD, Evaluation harnesses, LangSmith, Langfuse, Arize, PII handling, Guardrails, Azure AI Foundry, GCP Vertex AI

Prepare for this role

Recommended resources to build the skills for this position. Sponsored.

Top 50 LLM System Design Interview Questions

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Curated LLM system design questions from Zenaique.

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Curated embeddings questions covering models, similarity, and retrieval.

Top 15 Embeddings Interview Questions (Quick)

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A shorter embeddings interview prep set for quick review.

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