Artificial Intelligence Specialist

Elevation Capital; Bengaluru; Presencial; INR 4.500.000 - 7.000.000; Jornada completa

Descripción de la vacante

Elevation Capital seeks an AI Architect to define and execute our end-to-end AI strategy, architecture, and platform infrastructure. This role combines hands-on design of autonomous agent networks with leadership to scale enterprise-grade AI systems across the organization.

You will own the company-wide AI vision, collaborating with executive leadership, product, engineering, and security teams to deliver resilient, deterministic, high-performance AI solutions.

Formación

• 8+ years in distributed backend, platform, or cloud infrastructure engineering.
• 2+ years designing, building, and scaling production platforms and agentic workflows.
• Experience with agentic tech stacks and frameworks (LangGraph, AutoGen, CrewAI, LlamaIndex).
• Hands-on experience with model optimization and runtime (TensorRT, ONNX, vLLM).
• Experience with vector databases and knowledge graphs for complex retrieval.
• Cloud-native distributed systems, microservices, and container orchestration.
• Strong leadership and ability to influence cross-functional teams.
• B.Tech / M.Tech in Computer Science, AI, ML or equivalent.

Responsabilidades

• Architect, deploy multi-agent networks with scalable reasoning and tool execution.
• Design cognitive loops and interfaces (MCP) for secure APIs, databases, and code executors.
• Engineer hybrid state and memory systems for advanced AI workflows.
• Own and optimize cloud-native AI platform infrastructure across AWS/GCP/Azure.
• Drive model optimization, quantization, distillation, and runtime acceleration.
• Establish AI reliability practices with guardrails, telemetry, and safety controls.

Conocimientos

8+ years distributed backend

2+ years production platforms & agentc

Leadership & influence

Education in CS/AI/ML

Educación

B.Tech / M.Tech in Computer Science, AI, ML

Herramientas

LangGraph

AutoGen

CrewAI

LlamaIndex

TensorRT

ONNX

vLLM

Descripción del empleo

Role description: This role is with one of our portfolio companies.

About the Role

We are seeking a high-conviction AI Architect to define and execute our end-to-end AI strategy, architecture, and platform infrastructure. This role sits at the intersection of deep technical craftsmanship and strategic organizational ownership- combining hands-on architecture of autonomous agentic systems with the leadership required to scale enterprise-grade AI infrastructure.

In this position, you will own the company-wide AI vision, leading the shift from basic LLM integration to production-grade autonomous agent networks, self-correcting cognitive loops, tool-augmented execution models, and scalable platform infrastructure. You will work directly with executive leadership, product, engineering, and security teams to translate ambitious business goals into resilient, deterministic, and high-performance AI solutions.

Key Responsibilities

Agentic AI & Cognitive Architecture

• Autonomous Agent Workflows: Architect, orchestrate, and deploy multi-agent networks (hierarchical, swarm, supervisor-worker patterns) capable of multi-step reasoning, dynamic planning, and tool execution.
• Reasoning & Tool Execution: Design robust ReAct, ReFlect, and Plan-and-Execute cognitive loops. Build standard interfaces (including Model Context Protocol / MCP) and sandboxed environments for secure interaction with APIs, databases, code interpreters, and enterprise SaaS.
• Context & Memory Systems: Engineer hybrid state and memory management architectures integrating episodic/procedural memory, short-term context compression, and advanced graph-augmented RAG setups.

AI Infrastructure, Platform & Reliability

• Enterprise Infrastructure: Own, scale, and optimize cloud-native AI platform infrastructure across AWS, GCP, or Azure for low-latency, high-availability LLM and multimodal inference.
• Model Optimization & Fine-tuning: Drive model fine-tuning, quantization, distillation, and runtime acceleration using frameworks such as TensorRT, ONNX, vLLM, and open-source model ecosystems (Llama, Mistral, Qwen).
• Observability, Safety & Guardrails: Establish comprehensive AI reliability engineering practices-implementing deterministic guardrails, evaluation pipelines (Ragas, TruLens, DeepEval), telemetry/tracing (LangSmith, Phoenix), and robust defenses against prompt injection and data leakage.

Key Requirements

• Core Experience: 8+ years overall in distributed backend, platform, or cloud infrastructure engineering, with 2+ years of hands-on experience designing, building, and scaling production platforms and agentic workflows.
• Agentic Tech Stack & Frameworks: Deep proficiency with LangGraph, AutoGen, CrewAI, LlamaIndex, or custom state-machine agent orchestrators.
• Model Optimization & Runtime: Direct experience with model acceleration and serving frameworks (TensorRT, ONNX, vLLM, Ollama) along with quantization, fine-tuning (LoRA/PEFT), and distillation techniques.
• Data & Retrieval Architecture: Proven experience with vector databases (Pinecone, Qdrant, Milvus, pgvector) and Knowledge Graphs for complex retrieval setups.
• Cloud & Distributed Systems: Hands-on experience with cloud-native distributed architecture, asynchronous event-driven microservices (Kafka, RabbitMQ), streaming protocols (WebSockets, SSE), and container orchestration (Kubernetes, Docker sandboxes).
• Leadership & Influence: Outstanding stakeholder management skills with a track record of driving cross-functional technical consensus and articulating AI strategy to C-suite partners.
• Education: B.Tech / M.Tech in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience.