Antino - AI Solution Architect

Antino seeks a hands-on AI Solution Architect in Gurgaon/Gurugram, India, to represent its AI practice with enterprise clients and build the solutions proposed. The role spans technical pre-sales, architecture, proofs of concept and production delivery, working with clients, sales, engineering and data science teams.

Responsibilities

• Lead discovery calls and workshops; contribute to RFP/RFI responses, proposals, statements of work, estimates and ROI discussions. Translate business challenges into AI/ML, generative AI and agentic AI solutions, explaining options, risks and trade-offs to executives and technical stakeholders.
• Code and demonstrate rapid proofs of concept. Design scalable architectures using LLMs, RAG, agents, embeddings, vector databases, knowledge graphs and prompt/context engineering. Choose among classical ML, fine-tuning, RAG, agents and hybrid approaches.
• Build and deploy on AWS, Azure or GCP; design APIs, data pipelines, model serving, microservices and enterprise integrations. Develop production agent workflows with tool calling, memory, human oversight and agent hand-offs. Review systems for security, scalability, reliability, performance, observability, evaluation and cost.
• Guide teams from proof of concept to production; establish monitoring, guardrails, evaluation and LLMOps practices; train colleagues and create reusable architectures, accelerators and playbooks.

Required qualifications

• At least 5 years in software engineering, data science, ML engineering or solution architecture, including at least 2 years designing and deploying GenAI/LLM solutions in production. The site also displays an overall 5–10-year experience range; structured posting metadata lists a bachelor's degree.
• Hands-on agentic AI experience and foundations in statistics, classical ML, deep learning, feature engineering, model evaluation and time series. Strong Python and API development skills, preferably FastAPI, plus client-facing communication skills.
• Experience with LLM platforms, agent frameworks, vector/search technologies, advanced RAG, MLOps/LLMOps, containers, Kubernetes, CI/CD, distributed systems and secure enterprise architecture. Advanced areas described include MCP and A2A, model adaptation and inference optimization, evaluation tooling, prompt-injection defense, privacy and AI governance.

Preferred qualifications

• Consulting for international clients; enterprise knowledge platforms; production Voice AI, Document AI or computer vision; Databricks, Snowflake, BigQuery or Spark; relevant industry exposure; cloud certifications; or published technical contributions.