AI Solution Architect
First seen by Alion on Sep 29, 2026.
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 :
1. 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.
2. 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.
3. 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.
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.
Skills
Python, FastAPI, LangChain, LangGraph, Machine Learning, Generative AI, ElasticSearch, Kubernetes, Docker