Summary:
We are seeking a highly skilled AI Solutions
Architect to build production-ready, scalable AI solutions across the full AI
stack from infrastructure and data readiness to model selection, orchestration,
application design, and operational scaling. You will be the technical backbone
ensuring AI initiatives move from POC to production and scale safely, with
cost, security, performance, and governance built in from day one.
Responsibilities :
Design end-to-end reference architecture covering AI infrastructure, model selection, data readiness, orchestration (RAG, agents, multi-step reasoning), and application layer integrations
Define clear architecture patterns across the full delivery journey from POC to production hardening to enterprise scale
Balance performance, latency, cost, and risk in every design decision and create reusable blueprints and checklists for delivery teams
Ensure production readiness across security, monitoring, fallback strategies, data governance, and compliance before solutions reach client environments
Benchmark and evaluate emerging AI tools and models including OpenAI, Anthropic, Hugging Face, and custom-built frameworks for applicability and integration
Perform in-depth research to stay current on advancements in generative AI, NLP, computer vision, and agentic AI, and translate findings into actionable architecture decisions
Collaborate with stakeholders to assess business requirements and translate them into concrete AI strategies and implementation roadmaps
Lead pre-sales solutioning efforts - AI discovery workshops, architecture proposals, and client-facing technical presentations
Guide engineering teams with clear architecture direction, review designs, implementation approaches, and agent workflows
Step in hands-on to resolve complex architectural, integration, and orchestration challenges when teams are blocked
Partner with the Delivery Manager to manage risks, dependencies, and delivery escalations proactively
Implement responsible AI protocols addressing governance, ethical usage, bias detection, and regulatory compliance
Identify data quality and privacy risks, and ensure data security standards are upheld across all AI solutions
Act as a mentor for offshore AI developers and engineers, building skills in prompt engineering, RAG, agent design, and LLMOps
Facilitate cross-disciplinary collaboration with data scientists, engineers, product managers, and business stakeholders to drive project success
Conduct periodic evaluations of deployed AI systems, recommending enhancements for improved performance and operational efficiency.
Requirements
15+ years of overall IT experience with at least 5 years of hands-on experience in GenAI, ML engineering, and agentic AI design and implementation, not just advisory
Strong background in application development, enterprise integrations, and distributed systems
Proven experience designing end-to-end AI solutions including LLMs (OpenAI, Anthropic, open-source models), embeddings, vector databases, and retrieval systems
Hands-on experience with agentic and orchestration frameworks such as LangGraph, LangChain, or Semantic Kernel, including multi-agent design, tool routing, and memory management
Familiarity with foundational models including GPT, BERT, Llama, and Mistral, and their customization for enterprise use cases
Proficiency in AI/ML frameworks such as TensorFlow, PyTorch, and Hugging Face
Skills in cloud AI platforms (Azure, AWS, GCP) with practical understanding of deployment trade-offs between managed APIs and self-hosted models
Understanding of MLOps and LLMOps principles, including CI/CD for AI, eval-driven deployments, prompt versioning, and production monitoring
Experience with pipeline orchestration tools such as Airflow or Kubeflow, and container platforms including Docker and Kubernetes
Expertise in responsible AI governance, ethical frameworks, bias detection, and compliance with data privacy regulations (GDPR, CCPA)
Strong ability to lead and mentor technical teams, and communicate complex AI concepts clearly to both engineering teams and business stakeholders
Experience with CRM or ERP platforms (Salesforce, SAP) is an advantage
