Antino Labs - AI Solution Architect

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)