Solutions Architect

Etenico Technologies; Bengaluru; On-site; INR 350,000 - 550,000; Full time

Job summary

Etenico Technologies seeks a veteran Enterprise AI Architect to define and lead scalable AI systems across retrieval, graph-based reasoning, and multi-agent orchestration. You will shape architecture, standards, and roadmaps, mentoring teams to operationalize cutting-edge AI.

The role emphasizes secure, production-ready pipelines and cost-aware design. Ideal candidates bring 10+ years in Data Science, ML, and Enterprise Architecture, with hands-on experience in vLLM, Graph databases, MoE

Qualifications

• Advanced degree in CS/AI with research emphasis is required.
• 10+ years in Data Science, ML, and Enterprise Architecture.
• Experience architecting and deploying production-grade AI/ML systems at scale.

Responsibilities

• Define enterprise AI architecture, standards, and roadmaps.
• Design retrieval pipelines optimized for accuracy, latency, and cost.
• Lead hybrid retrieval systems including Graph RAG andNeo4j-based solutions.
• Architect multi-agent harnessing with secure tool routing and guardrails.
• Own inference architecture and latency/throughput optimizations.
• Lead multi-GPU training with MoE and distributed strategies.
• Define ML Ops standards for CI/CD and production readiness.

Skills

Enterprise AI Architecture

Advanced Retrieval & Graph RAG

Agent Harness Engineering

Inference & Runtimes

Distributed Systems

Research Operationalization

Leadership & Mentorship

Education

ME/MTech/MCA/PhD in CS/AI

PhD preferred for leadership

Tools

Neo4j

Amazon Neptune

LangGraph

AutoGen

Crew AI

vLLM

SG Lang

TensorRT

TensorRT-LLM

Job description

Educational Qualification

ME / MTech / MCA / PhD in Computer Science, AI, or a related field.

PhD highly preferred for advanced architectural leadership and research translation.

Experience

10+ years in Data Science, Machine Learning, and Enterprise Architecture.

Extensive proven experience architecting, scaling, and deploying production-grade AI/ML, LLM/SLM, and complex retrieval systems at the enterprise level.

Work Schedule

Day Shift

Key Responsibilities

Architecture & Strategy: Define the enterprise architecture, standards, and roadmap for AI systems. Design models and retrieval pipelines optimized for accuracy, latency, and cost across diverse hardware envelopes (e.g., single-GPU, A100/H200 inference, and training envelopes). Establish the enterprise ontology and knowledge representation strategy.

Graph RAG & Advanced Retrieval Architecture: Lead the architectural design of hybrid retrieval systems. Scale Vector and Graph Databases (e.g., Neo4j) to handle massive enterprise corpora. Design systems that seamlessly route between standard semantic search and Graph RAG (community detection, multi-hop reasoning) based on query complexity.

Agent Harness Engineering Orchestration: Architect the enterprise

standard for multi-agent workflows (e.g., LangGraph, AutoGen). Design the core

"Harness" robust state management, distributed memory architectures, secure tool-execution sandboxes, and deterministic guardrails (input/output validation, PII redaction, hallucination checks) that make autonomous agents safe for production.

Inference, Serving Optimization: Own inference architecture using modern serving engines (vLLM, SG Lang). Drive latency/throughput optimization via continuous batching, quantization, speculative decoding, Flash Attention, and optimized runtimes (Tensor RT / Tensor RT-LLM).

Distributed Training Scaling: Lead multi-GPU training architectures (DDP / FSDP), implementing data- and model-parallel strategies, gradient accumulation, and Mixture-of-Experts (MoE) scaling efficiency for specialized SLMs.

Cloud, ML Ops & Production Readiness: Architect secure AI solutions on AWS or Azure. Define enterprise ML Ops standards for CI/CD, adapter versioning, checkpointing, and CI/CD for agentic pipelines and knowledge graph updates.

Leadership & Mentorship: Act as the technical authority and design-review leader. Mentor Data Scientists and ML Engineers, fostering a research-driven culture that rapidly operationalizes the latest AI advancements.

Required Skills & Expertise

Enterprise AI Architecture: Cloud-based ML architecture (AWS/Azure), ML Ops, enterprise security, and AI governance.

Advanced Retrieval & Graph RAG: Deep architectural knowledge of Knowledge Graphs (Neo4j, Amazon Neptune), Vector Databases at scale, ontology design, and Microsoft Graph RAG or custom map-reduce graph reasoning patterns.

Agentic Frameworks & Harnessing: Multi-agent orchestration (Lang Graph, Auto Gen, Crew AI), enterprise harness engineering, secure tool routing, state persistence, and Guardrails AI/NeMo Guardrails.

Inference & Runtimes: Deep expertise in vLLM, SG Lang, Tensor RT, Tensor RT- LLM, and hardware‑aware tuning.

Distributed Systems & Model Internals: Multi-GPU training (DDP/FSDP), Mixture-of-Experts (MoE), sparse/long-context attention, and Flash Attention.

Advanced Research Operationalization: Strong track record of adapting complex research concepts into scalable enterprise pipelines.

Preferred / Nice to Have

Domain Knowledge: Strong preference for experience within the Healthcare and Medical domain, specifically with Revenue Cycle Management (RCM).

Deep knowledge of speculative decoding, structured/guided decoding, and KV-cache optimization.

Responsible AI frameworks, model risk management, and regulatory compliance.

Experience defining AI strategy at an organizational or platform level.