Solution Architect – Enterprise Applications with AI/ML Exposure

Job Title: Solution Architect – Enterprise Applications with AI/ML Exposure

Experience: 8–10 Years

Location: Thiruvananthapuram

Employment Type: Full-time (WFO)

Salary Offered : Max 22 LPA

Job Summary

We’re looking for a talented Solution Architect with a strong foundation in designing and developing

large-scale enterprise applications, and a growing interest or experience in modern AI/ML-driven

technologies. This role is ideal for someone who is confident in architecture, passionate about

emerging trends like AI/ML, and eager to help shape intelligent systems in collaboration with

engineering and business teams.

• Design scalable, secure, and maintainable enterprise application architectures.
• Translate business needs into clear technical solutions and design patterns.
• Lead design discussions, code reviews, and solution planning with internal teams.
• Guide development teams by providing architectural direction and mentoring.
• Collaborate with DevOps for smooth deployment and CI/CD implementation.
• Participate in client meetings and technical solutioning discussions.
• Explore and propose the use of AI/ML capabilities where relevant, especially in areas like

intelligent search, automation, and data insights

Must-Have Skills & Qualifications

• 8–10 years of experience in software development and solution architecture.
• Hands-on expertise in either Python or C# .Net.
• Deep understanding of software architecture patterns—microservices, event-driven, layered
• designs.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Solid knowledge of databases (SQL & NoSQL), APIs, and integration techniques.
• Exposure to or strong interest in AI/ML technologies—especially those involving intelligent
• automation or data-driven systems.
• Good interpersonal and communication skills; experience interfacing with clients.
• Capability to lead technical teams and ensure delivery quality.

Preferred Skills

• Awareness of LLMs, vector databases (e.g., Pinecone, FAISS), or RAG-based systems is a plus.
• Familiarity with Docker, Kubernetes, or DevOps workflows.
• Knowledge of MLOps or experience working alongside data science teams.
• Certifications in cloud architecture or AI/ML are a bonus.