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Senior Software Engineer- AI Applications

Saitc

  • Hyderabad, India
  • 12-14 Lacs PA
  • 4-6 Yrs
  • Posted Today
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About the role

Senior Software Engineer AI Applications Location: Hyderabad, India Employment Type: Full-Time, On-site Experience: 4+ Years Expected CTC: 12-14 LPA About SAITC SAITC is an AI software development company based in Saudi Arabia, focused on building production-grade AI solutions for businesses. We build AI chatbots, RAG applications, AI agents, AI-powered SaaS products, enterprise integrations, and custom AI software. We are looking for a Senior Software Engineer who can take ownership of the backend architecture and software engineering behind these solutions. Role Overview This is a hands-on technical leadership role. You will design and build production software, make architectural decisions, work closely with our Applied AI team, review code, and guide other engineers. The ideal candidate is a strong backend engineer with practical AI application experience who understands how to turn AI capabilities into reliable, scalable, and production-ready software. Key Responsibilities 1. Backend Engineering & Architecture Design scalable backend architectures for AI applications, SaaS platforms, APIs, and enterprise solutions. Build production services primarily using Python and FastAPI. Design REST APIs, business logic, databases, integrations, and background processes. Design and optimize PostgreSQL databases and data models. Build integrations with third-party APIs, ERP/CRM systems, Odoo, and customer databases. Design secure multi-tenant systems with proper customer data isolation. 2. AI Application Engineering Work closely with Applied AI Engineers to integrate AI capabilities into production software. Build applications using LLM APIs, RAG, embeddings, vector databases, AI agents, and AI-powered workflows. Integrate providers such as OpenAI, Google Gemini, OCI Generative AI, and open-source models. Design APIs and backend workflows around AI services, including streaming, retries, fallbacks, and cost/latency considerations. Ensure AI systems are securely integrated with customer and business data. 3. Technical Leadership Establish coding and architectural standards across projects. Conduct code reviews and maintain high engineering quality. Mentor engineers and provide technical direction. Convert business and client requirements into practical technical solutions. Participate in technical discussions with founders and clients when required. 4. Security, Testing & Quality Implement authentication, authorization, RBAC, tenant isolation, and API security. Ensure appropriate unit, integration, and API testing for backend services. Work closely with the DevOps/QA Engineer on deployment, monitoring, testing, and production issues. Required Skills & Experience 4+ years of professional software development experience. Strong experience with Python and FastAPI. Strong PostgreSQL and database design skills. Strong understanding of backend architecture and REST APIs. Experience building and deploying production software. Practical experience with LLM APIs, RAG, embeddings, vector databases, or AI agents. Understanding of authentication, authorization, and application security. Experience with Docker and Linux. Strong Git/GitHub experience. Good understanding of asynchronous programming, APIs, queues, and caching. Strong problem-solving and debugging ability. Ability to lead technical decisions and mentor other engineers. Good to Have Redis / Celery / RabbitMQ / Kafka AWS / OCI / GCP / Azure Kubernetes / Terraform Experience building AI SaaS products Experience with enterprise integrations Experience working directly with clients What Success Looks Like Within the first 612 months, you will help SAITC establish a strong technical foundation where: AI applications follow consistent and scalable architectures. Backend components can be reused across products and clients. AI services integrate cleanly with business applications and data. Customer data is securely isolated. Engineering standards and code quality improve across the team. SAITC can build and deliver new AI solutions faster. Education A Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field is preferred.

Eligibility & process

Who can apply

Experience: 4-6 Yrs. Open to matching qualifications.

Skills & stack

Docker Development AWS Application MongoDB Machine Full Stack SQL

Selection process

  1. Online Assessment / Technical Interview Rounds / HR Review

Prep kit

20 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 (Docker, Development, AWS, Application, MongoDB, Machine, Full Stack, SQL) & 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 Saitc Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
What is the difference between WHERE and HAVING clauses in SQL? Answer
Model answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer
Model answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer
Model answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer
Model answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer
Model answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Behavioural & HR questions 3
Why do you want to join Saitc as a Senior Software Engineer- AI Applications?
Highlight Saitc's market reputation, recent tech innovations, and how your skills in Docker 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.
Saitc 12-14 Lacs PA
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