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.
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
Strong understanding of:
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.

About Antino
Antino is an AI-native technology consulting company helping organisations embed intelligence into the way they operate.
600+ Engineers | 50 AI Specialists | 400 Projects Delivered | 20 Countries Served
With offices across India, the US, UK, and UAE, Antino has developed Company Brain—a governed intelligence layer connecting enterprise knowledge, people, systems, and workflows to enable smarter decisions and coordinated action.