Antino Labs - AI Solution Architect

Antino; Gurugram District; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Antino in India seeks a hands-on AI Solution Architect to lead technical discovery, design GenAI/Agentic AI solutions, and guide them from POC to production. You will build POCs, run demos, write code, and present trade-offs to CXOs while coordinating with sales, engineering and data science teams.

The role demands 5+ years in software/ML, hands-on Python, and experience deploying GenAI/LLM solutions and agentic systems across cloud platforms.

Qualifications

• 5+ years in software engineering, data science, ML engineering, or solution architecture.

Responsibilities

• Lead technical discovery calls, workshops, RFP/RFI responses, proposals, SOWs, estimates, and ROI discussions.

Skills

Python

API development

LLM/GenAI design

Client-facing communication

Cloud architecture

MLOps/LLMOps

Tools

OpenAI

Azure OpenAI

Anthropic Claude

Gemini

AWS Bedrock

LangGraph

LangChain

LlamaIndex

CrewAI

OpenAI Agents SDK

Claude Agent SDK

Google ADK

Pinecone

Weaviate

Milvus

Qdrant

pgvector

FAISS

Elasticsearch/OpenSearch

Docker

Kubernetes

Job description

About The Role

We are looking for a hands‑on AI Solution Architect who can act as the technical face of Antino's AI practice with enterprise clients and also build what is proposed. You will lead technical discovery and pre‑sales discussions, understand business challenges, design AI/GenAI/Agentic AI solutions, estimate effort, explain trade‑offs and risks, and work closely with clients, sales, engineering, and data science teams. This is not a slides‑only role. You will build POCs, run demos, write code, define architecture, review production systems, and guide solutions from POC to production.

Key Responsibilities

• Pre‑Sales & Client Solutioning :
• Lead technical discovery calls, workshops, RFP/RFI responses, proposals, SOWs, estimates, and ROI discussions.
• Translate business problems into practical AI/ML, GenAI, and Agentic AI solutions.
• Present solutions and technical trade‑offs to CXOs, architects, security, data, and engineering teams.
• Build rapid POCs and demos to validate and de‑risk solutions.
• Solution Architecture & Delivery :
• Design scalable AI architectures using LLMs, RAG, AI Agents, vector databases, embeddings, prompt/context engineering, and knowledge graphs.
• Select the right approach across classical ML, LLMs, fine‑tuning, RAG, agents, or hybrid solutions.
• Build and deploy solutions across AWS, Azure, or GCP.
• Design APIs, data pipelines, model serving, enterprise integrations, and microservices.
• Build production‑grade agentic systems including tool calling, multi‑agent workflows, memory, human‑in‑the‑loop, and agent hand‑offs.
• Establish best practices for security, scalability, performance, observability, evaluation, reliability, and cost optimisation.
• AI Engineering & Team Enablement :
• Stay hands‑on with Python, FastAPI, AI frameworks, LLM platforms, and cloud technologies.
• Guide engineering and data science teams from POC through production.
• Implement AI evaluation, monitoring, guardrails, and LLMOps practices.
• Train internal teams and create reusable reference architectures, accelerators, and playbooks.
• Track emerging AI models, frameworks, protocols, and research and apply relevant innovations to Antino's AI practice.

Must‑Have Skills & Experience

• 5+ years in software engineering, data science, ML engineering, or solution architecture.
• 2+ years of hands‑on experience designing and deploying GenAI/LLM solutions in production.
• Strong experience building Agentic AI, including tool‑calling agents, multi‑agent systems, agentic RAG, memory, and orchestration.
• Strong foundations in statistics, classical ML, deep learning, feature engineering, model evaluation, and time series.
• Strong hands‑on Python and API development, preferably FastAPI.
• Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, AWS Bedrock, and open‑weight models such as Llama, Qwen, Mistral, or DeepSeek.
• Experience with frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, Claude Agent SDK, or Google ADK.
• Experience with vector/search technologies such as Pinecone, Weaviate, Milvus, Qdrant, pgvector, FAISS, Chroma, Elasticsearch/OpenSearch.
• Strong knowledge of Advanced RAG, GraphRAG, hybrid search, re‑ranking, query rewriting, multimodal RAG, and context engineering.
• Experience with MLOps/LLMOps, Docker, Kubernetes, CI/CD, model serving, monitoring, evaluation, and cost optimisation.
• Strong understanding of distributed systems, microservices, databases, APIs, and secure enterprise architecture.
• Strong client‑facing and communication skills with the ability to explain complex technical concepts to both CXOs and engineering teams.

Advanced AI Expertise

• Agent Design : Tool/function calling, ReAct, planning, reflection, memory, state management, HITL, failure recovery.
• Multi‑Agent Systems : Supervisor, hierarchical and peer‑to‑peer patterns, task routing and agent hand‑offs.
• Agent Protocols : MCP and A2A.
• RAG : Hybrid search, re‑ranking, GraphRAG, knowledge graphs, agentic/multimodal RAG, permission‑aware retrieval.
• Model Adaptation : Reasoning, long‑context and multimodal models, SLMs, LoRA/QLoRA, distillation, prompting vs RAG vs fine‑tuning.
• Inference : vLLM/TGI, quantisation, prompt caching, model routing, latency and token‑cost optimisation.
• Evaluation : Ragas, DeepEval, LangSmith, Langfuse, Arize Phoenix, golden datasets, LLM‑as‑a‑Judge, tracing.
• AI Security & Governance : Prompt injection defence, guardrails, PII protection, OWASP Top 10 for LLMs, NIST AI RMF, ISO/IEC 42001, EU AI Act, and India's DPDP Act.

Good To Have

• Experience in IT services/consulting with global clients across the US, UK, or Middle East.
• Experience with enterprise knowledge platforms, knowledge graphs, or Company Brain‑style systems.
• Production experience in Voice AI, Document AI, or Computer Vision.
• Experience with Databricks, Snowflake, BigQuery, or Spark.Domain exposure to BFSI, Healthcare, Retail, Logistics, or Manufacturing.
• AWS, Azure, or Google Cloud certifications.
• Open‑source contributions, research papers, patents, technical blogs, or conference talks.

(ref:hirist.tech)