Job Location (Short): Hyderabad, India

Workplace Type: Hybrid

Req Id: 3168

Responsibilities

Role Overview

We are looking for a Senior AI Engineer to join the Octave InConcert AI product development team within Octave’s Engineering Information Management ecosystem. This role focuses on building production-grade LLM-powered agentic systems, including industrial AI assistants, RAG/Graph RAG retrieval systems, and document intelligence pipelines operating on complex engineering datasets (e.g., P&IDs;, technical documents, asset metadata). In addition to LLM-based solutions, the role also contributes to classical machine learning and deep learning use cases such as computer vision for engineering drawings and predictive modelling.

Key Responsibilities

Design, develop, and deploy agentic LLM systems, including:
Tool usage and integrations
Multi-step workflows
Agent skills and MCP integrations
Build and optimize retrieval pipelines, including:
Chunking and embedding strategies
Hybrid search systems
Knowledge graphs and GraphRAG approaches
Ensure LLM quality and reliability, including:




Evaluation datasets and benchmarking
Regression testing
Human-in-the-loop validation
Industrialize AI solutions under enterprise constraints:
Cost and latency optimization
Security, governance, and data sovereignty
Observability and guardrails
Lead proof-of-concepts (PoCs) and feasibility studies and collaborate with stakeholders
Contribute to broader ML/DL initiatives as required
Drive AI-first development practices, leveraging agent-based coding tools to:
Accelerate development workflows
Maintain agent-friendly code, specs, and issue definitions
Stay updated with the latest advancements in LLMs, agents, and GenAI ecosystems, and share knowledge across the organization

Education / Qualifications

Must-Have Qualifications

Bachelor’s or Master’s degree in computer science or a related field
5+ years of Python development experience, including:
Solid software engineering fundamentals
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📌 Software Lead (Hyderabad)
🏢 Octave
📍 Hyderabad