AI Solution Architect

The role

You will be the technical face of Antinos AI practice in front of clients and also the person who builds what was promised. You will join pre-sales calls with enterprise clients understand their business problem and propose a credible AI or agentic solution during the conversation with trade-offs risks and a rough effort estimate. After the deal you stay hands-on. You will build the proof of concept set the architecture and guide engineering and data science teams until the solution runs in production. You will also train Antinos internal teams so that the whole practice gets stronger over time.

A hands-on architect role This is not a slides-only role. We expect you to write code run demos and review production systems yourself.

Who you are The ideal candidate is an architect and a builder at the same time. PROFILE WHAT WE LOOK FOR Tier-1 background You studied at a Tier-1 institute (IIT NIT BITS IIIT or equivalent) or you built AI solutions at Tier-1 organisations such as top product companies AI-first startups or the AI practices of leading consulting firms. Client-ready You are comfortable in front of CXOs product heads and technical teams. You can listen to a business problem and sketch a sound architecture on the spot. Data science GenAI You know classical ML and statistics well enough to tell when an LLM is the wrong tool and agentic AI well enough to build multi-agent systems that hold up in production. Hands-on You have personally built and shipped agentic AI solutions not only designed them. A multiplier You enjoy teaching and you raise the bar of the engineers and data scientists around you. ANTINO CAREERS

AI SOLUTION ARCHITECT

03 What youll do PRE-SALES Pre-sales and client solutioning Lead technical discovery calls and workshops with enterprise clients alongside sales and business development. Turn business problems into AI solution designs during the conversation covering architecture trade-offs risks and rough effort. Answer deep technical questions from client architects security teams and data teams with confidence. Own the technical side of RFP and RFI responses proposals SOWs estimates and ROI cases. Build demos and proofs of concept quickly often within days to win and de-risk deals.

ARCHITECTURE Solution architecture Design end-to-end AI/ML Generative AI and agentic AI architectures for enterprise use cases. Architect solutions using LLMs RAG AI agents vector databases embeddings and prompt and context engineering. Choose the right approach for each problem: classical ML an LLM a fine-tuned model an agent or a mix. Evaluate and select LLMs frameworks databases and cloud services on quality latency cost and data residency. Design and implement on AWS Azure or GCP: data pipelines model serving APIs and client-system integration.

DELIVERY Building and delivery Build agentic systems hands-on: tool-using agents multi-agent workflows memory human-in-the-loop steps and integrations. Guide engineering and data science teams from proof of concept to production. Set best practices for scalability performance security observability evaluation and cost optimisation. Put guardrails evaluation suites and monitoring in place so AI systems stay reliable after go-live. Write clear architecture documents and review designs and code.

ENABLEMENT Team enablement Train Antinos internal engineering and data science teams on GenAI agentic AI and AI engineering practices. Build reusable reference architectures accelerators and playbooks that speed up future projects. Contribute to Antinos own AI products including Company Brain. Track new models frameworks and research and bring what matters into Antinos practice. 04 Must-have skills and experience

AREA REQUIREMENT

Experience 5 years in software engineering data science ML engineering or solution architecture with at least 2 years designing and shipping GenAI or LLM solutions to production. Agentic AI A proven track record of building agentic AI solutions that real users rely on beyond proofs of concept: tool-calling agents multi-agent workflows agentic RAG. ANTINO

CAREERS

AI SOLUTION ARCHITECT 3 AREA REQUIREMENT

Data science Strong foundations in statistics classical ML (regression gradient boosting time series) deep learning feature engineering and model evaluation. Pre-sales Client-facing experience with discovery calls solution proposals effort estimates and technical presentations to senior stakeholders. Engineering Strong hands-on Python and API development (e.g. FastAPI).

You can build a working proof of concept yourself. LLM platforms OpenAI Azure OpenAI Anthropic Claude Google Gemini and AWS Bedrock plus open-weight models such as Llama Qwen Mistral or DeepSeek. Frameworks LangGraph LangChain LlamaIndex CrewAI OpenAI Agents SDK Claude Agent SDK or Google ADK. Retrieval Vector databases and search: Pinecone Weaviate Milvus Qdrant pgvector FAISS Chroma or Elasticsearch/OpenSearch. Cloud Architecture on AWS Azure or GCP including SageMaker Bedrock Azure AI Foundry and Vertex AI. MLOps

LLMOps Model deployment CI/CD Docker Kubernetes monitoring and cost tracking. Systems Microservices distributed systems databases and secure scalable architectures. Communication You can explain trade-offs to a CXO and to an engineer and write architecture documents and proposals that people act on. 05 Advanced AI depth we expect You should be able to discuss each of these areas with a client and apply them in a build. AREA WHAT YOU SHOULD KNOW WELL Agent design Tool and function calling planning and reflection patterns (ReAct plan-and-execute) short- and long term memory state management human-in-the-loop approvals failure recovery. Multi-agent systems Orchestration patterns (supervisor hierarchical peer-to-peer) task routing agent hand-offs keeping cost and latency under control as agents multiply.

Agent protocols Model Context Protocol (MCP) for connecting agents to tools and data Agent2Agent (A2A) for interoperability secure integration with enterprise systems. Advanced RAG Chunking strategies hybrid search re-ranking query rewriting GraphRAG and knowledge graphs agentic and multimodal RAG permission-aware retrieval context engineering. Model adaptation Reasoning long-context and multimodal models small language models fine-tuning (LoRA QLoRA) distillation and when to choose prompting RAG or fine-tuning. Inference

LLMOps Serving with vLLM or TGI quantisation prompt caching model routing token economics latency and cost optimisation.

ANTINO CAREERS AI SOLUTION ARCHITECT 4 AREA WHAT YOU SHOULD KNOW WELL Evaluation Offline and online evals golden datasets LLM-as-judge Ragas DeepEval LangSmith Langfuse or Arize Phoenix tracing agent runs. Security governance Prompt injection defence guardrails PII redaction OWASP Top 10 for LLM applications responsible AI NIST AI RMF ISO/IEC 42001 the EU AI Act and Indias DPDP Act. 06

Good to have Experience in an IT services or consulting firm working with global clients in the US UK or Middle East. Experience building enterprise knowledge platforms knowledge graphs or company brain style systems. Voice AI document AI or computer vision solutions in production. Data platform experience with Databricks Snowflake BigQuery or Spark. Domain experience in BFSI healthcare retail logistics or manufacturing. AI or cloud certifications from AWS Microsoft Azure or Google Cloud. Public work such as open-source contributions research papers patents technical blogs or conference talks.

07 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 Company Brain With offices in India the US the UK and the UAE Antino developed Company Brain a governed intelligence layer that connects enterprise knowledge people systems and workflows. By carrying context across the organisation it enables AI to support decisions coordinate action and improve how work gets done

Required Skills:

AI SolutionAI/MLLLMAgentic AI & AI AgentsMulti-Agent Systems & Tool/Function CallingModel Fine-tuning LoRA/QLoRA & Model OptimizationAI Security Guardrails & Responsible AI