Hiring for AI Solutions Architect
Contract Role
9 Plus years of Exp
Gurgaon/Bangalore - Hybrid Working
Role Description
This is an on-site role for an AI Solutions Architect located in Bangalore or Gurgaon . The role will involve designing and developing AI-driven solutions tailored to meet business objectives. Key responsibilities include leading solution architecture efforts, consulting with clients to understand requirements, integrating AI technologies into existing workflows, and collaborating across teams to achieve seamless software development and business process improvements. The AI Solutions Architect is expected to bridge the gap between business needs and technical capabilities to drive innovation and efficiency.
Key Responsibilities
- Lead the end‑to‑end architecture and design of AI/ML solutions across multiple use cases.
- Translate business requirements into scalable, secure, and cost‑effective AI architectures.
- Design and oversee solutions involving Machine Learning, Deep Learning, Generative AI, NLP, and Computer Vision.
- Define AI solution blueprints, reference architectures, and technical standards.
- Work closely with data scientists, engineers, product teams, and business stakeholders.
- Evaluate and recommend AI platforms, frameworks, and tools.
- Ensure solutions follow best practices for MLOps, model deployment, monitoring, and governance.
- Guide teams on cloud‑native AI implementations (AWS / Azure / GCP).
- Conduct architecture reviews, risk assessments, and performance optimization.
- Stay current with emerging AI technologies and industry trends.
- Provide technical leadership and mentoring to junior architects and engineers.
Required Skills & Qualifications
- 9+ years of overall IT experience with strong focus on AI/ML solution architecture.
- Proven experience designing and delivering enterprise‑scale AI solutions.
- Strong understanding of:
- Machine Learning & Deep Learning frameworks (TensorFlow, PyTorch, Scikit‑learn)
- Generative AI & LLMs (RAG, prompt engineering, fine‑tuning concepts)
- Data engineering and pipelines
- Hands‑on experience with cloud platforms (AWS, Azure, or GCP).
- Experience with MLOps tools, CI/CD, and model lifecycle management.
- Strong knowledge of system architecture, microservices, APIs, and integration patterns.
- Excellent stakeholder management and communication skills.
- Ability to work in a fast‑paced, cross‑functional environment.
Preferred Qualifications
- Experience in large enterprise or consulting environments.
- Exposure to AI governance, security, and compliance frameworks.
- Certifications in Cloud Architecture or AI/ML.
- Experience working with global clients or multi‑geography teams.
