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AI-First Azure Solutions Architect & Engineer

Ajentica

  • Remote, India
  • Competitive Package (Best in Industry)
  • 8 Yrs
  • Posted Today
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About the role

AI-First Azure Solutions Architect & Engineer About Ajentica Ajentica is an AI-native product engineering company helping ambitious software businesses become truly agentic. We partner with growth-stage and private-equity-backed companies to modernize complex platforms, redesign operational workflows and build intelligent systems that work alongside people not merely generate content. Our work spans agentic automation, enterprise software modernization, intelligent product development and AI-enabled delivery across healthcare, pharmacy, compliance, HR technology and other operationally complex industries.

From HIPAA-sensitive clinical workflows and autonomous desktop agents to platform integration and AI-assisted software development, we focus on moving AI beyond prototypes and into secure, measurable and production-ready systems.

About the Role

Ajentica is looking for an experienced AI-First Azure Solutions Architect & Engineer who will architect and implement Azure and .NET solutions. This is not a purely advisory architecture position. We are looking for a hands-on architect and engineer who can be responsible for systems , understand systems end to end, translate business and technical requirements into architecture, create clear system designs and diagrams and implement to a high level of quality with AI tooling.

You will work primarily within the Microsoft ecosystem, with C#/.NET and Azure at the core , while using AI development tools such as Claude Code and Cursor as part of your everyday engineering workflow . What Youll Do Own end-to-end solution and system architecture, from requirements and system understanding through design, implementation, and production. Understand existing systems and translate requirements into clear architecture, system designs, technical specifications, diagrams, and integration approaches.

Define how solutions should be engineered , not simply what needs to be delivered. Implement designs/specs where asked to. You will be hands-on with C#, .NET, Azure, and SQL , both code and reviews.

Establish and enforce architecture principles, coding standards, design patterns, and engineering best practices. Design scalable, secure, maintainable cloud-native solutions on Microsoft Azure. Guide decisions around microservices, APIs, event-driven architecture, messaging, caching, security, observability, and system integration.

Work effectively with Azure services including App Services, Functions, Service Bus, Storage, Key Vault , Azure SQL, and monitoring/observability tools. Partner with engineering, product, DevOps, and business stakeholders to turn requirements into production-ready technical solutions. Mentor engineers and provide strong technical leadership across teams.

Use Cursor and/or Claude Code extensively for architecture, implementation, testing, code review, and engineering productivity. Contribute to platform modernization, cloud migration, production reliability, and long-term technology strategy. What Were Looking For Must Have 4 to 8 years of professional software engineering experience , including significant senior technical or architecture responsibility.

Proven experience as a Solutions Engineer/Architect, System Architect, Technical Architect, or equivalent architecture-focused engineering lead . Strong experience designing enterprise-scale systems and producing architecture/design documentation and diagrams. Deep hands-on experience with C# and .NET/.NET Core .

Strong, direct experience building solutions on Microsoft Azure . Good understanding of Azure application security and services such as Azure Key Vault , identity/access, configuration, and secure cloud architecture. Strong understanding of software architecture, SOLID principles, design patterns, domain-driven design, microservices, REST APIs, and event-driven systems.

You should grasp common terms in these areas beyond the textbook level. Strong SQL and relational database knowledge. Practical understanding of CI/CD, Azure DevOps, Git, Docker, and modern cloud delivery practices.

You do not need to be a DevOps engineer , but you must understand how well-designed applications operate within a modern cloud environment. Daily, practical experience using Cursor and/or Claude Code for software engineering. AI-assisted development is a core expectation of this role, not a nice-to-have.

Strong technical leadership with the confidence to establish standards, challenge poor engineering decisions, and ensure teams follow agreed architecture and engineering practices. Excellent spoken and written English , with the ability to communicate architecture clearly to technical and non-technical stakeholders. Ability to provide significant working-hour overlap with the US Eastern Time (ET) zone .

Good to Have BTech from a Tier 1-2 engineering institution and/or experience in a leading company. Hands-on Python experience. Experience with Microsoft Fabric and the broader Microsoft data ecosystem.

Experience designing or integrating AI/agentic systems. Experience with LLMs, vector databases, MCP, or AI/ML workloads. Infrastructure-as-Code experience with Terraform or ARM.

Experience with healthcare technology and standards such as HIPAA, HITRUST, or SOC 2. Experience working with distributed engineering teams and US-based stakeholders. The Person Were Looking For You are an architect who still understands what happens inside the codebase.

You don't stop at producing a diagram or recommending a technology. You can explain why a system should be designed a certain way, define how it should be implemented, and work alongside engineers to make sure it is actually built that way. You are comfortable making technical decisions, defending them with sound reasoning, mentoring engineers, performing and reviewing implementation, and stepping in when engineering practices drift from the agreed direction.

Most importantly, you combine system-level thinking, hands-on engineering credibility, and strong technical leadership in an AI-first development environment.

Eligibility & process

Who can apply

Experience: 8 Yrs. Open to matching qualifications.

Skills & stack

Bus Technical Architecture Azure Service Bus ASP.NET Core C# Microservices Event Driven Architecture Python

Selection process

  1. Online Assessment / Technical Interview Rounds / HR Review

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Bus, Technical Architecture, Azure Service Bus, ASP.NET Core, C#, Microservices, Event Driven Architecture, Python) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Ajentica Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Ajentica as a AI-First Azure Solutions Architect & Engineer?
Highlight Ajentica's market reputation, recent tech innovations, and how your skills in Bus directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Ajentica Competitive Package (Best in Industry)
Recruiter Resume Apply
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