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Applications open Full-Time Job

Applied AI Engineer

SP Software

  • Bengaluru, India
  • Competitive Package (Best in Industry)
  • 12-15 Yrs
  • Posted Oct 23, 2026
HR
Recruiter contact In post
Recruiter / hiring contact
sayini.prasanthi@spsoftglobal.com at SP Software

About the role

WE ARE HIRING FOR THE ONE OF MNC CLIENT, PAYROLL: SP SOFTWARE PVT LTD NOTICE PERIOD:IMMEDIATE TO 15 DAYS LOCATION:BANGLORE INTERVIEW MODE : F2F(27TH AND 29 OCT) Work Location and Address: Dell 10(5days WFO) Role & responsibilities : Job Description- Applied AI Engineer, Security Senior Principal Engineer (I9) | Individual Contributor | Security Resiliency Organization Position Summary We are seeking a Senior Principal Engineer to build the AI-native capabilities that replace how security work is performed today. Security processes today run across multiple functional teams, frequently duplicated with different tooling and different definitions of done. This role re-engineers and implements the rationalized version of those processes as AI-native workflows, working from an approved solution design and directly with the teams whose work is being agentified. The expectation is production-grade engineering. Systems are delivered tested, instrumented so that the agent operations team can monitor them, and documented sufficiently for another engineer to extend them. Defects found in production are resolved by this role at root cause. Required Skills and Qualifications 1 2+ years in software engineering with direct ownership of systems in production Agentic systems you personally built and shipped into production, not prototypes, notebooks, or proofs of concept Strong programming proficiency in Python and at least one of TypeScript/JavaScript, Go, or an equivalent language, with demonstrable code quality practice covering testing, review, refactoring, and maintainability Hands-on implementation experience across: Agent loops and tool calling: loop control, sub-agent delegation, state management and checkpointing, and idempotent handling of consequential actions Context engineering: retrieval and search, memory, grounding in enterprise data, prompt assembly, and structured or schema-constrained output Testing and validation: automated tests for behavior that varies between runs, regression suites, and validation against defined acceptance criteria Cost and latency engineering: token accounting, prompt and response caching, model routing with fallback, and context compaction Process re-engineering experience: analyzing an existing workflow and rebuilding it around what automation makes possible, rather than encoding current practice into software Experience replacing an existing manual or fragmented business process with software, working directly with the people who perform that process today Integration engineering: REST and GraphQL APIs, event-driven consumers, OAuth and scoped credential handling, schema and contract versioning, and resilient behavior under partial failure Observability and delivery engineering: structured logging, distributed tracing, and metrics for systems whose behavior varies between runs, plus CI/CD pipelines, automated quality gates, and containerized deployment Ability to work independently from written specification, deliver against committed timelines, and communicate progress and blockers without prompting Bachelors or masters degree in computer science or a related field, or equivalent practical experience Preferred Skills and Qualifications Experience applying AI within a security domain such as application security, DevSecOps, vulnerability management, code analysis, or software supply-chain security Experience building MCP servers or agent tooling, or extending AI coding assistants such as Claude Code, GitHub Copilot, or Cursor Working knowledge of agentic frameworks and orchestration libraries, and judgment about when to use them Experience with LLMOps in production, covering versioning, staged rollout, and regression detection Familiarity with the OWASP Top 10 for LLM Applications and with secure development practice Experience ramping quickly onto an unfamiliar codebase and becoming productive in it MANDATORY Technical Skills: AT LEAST 5+YRS IN Python, LangGraph, Lang Fuse, LLM must haves Typescript/JavaScript. Hands on Production based Implementation experience is mandate. Bachelors or masters degree in computer science or a related field, or equivalent practical experience IF ANYONE INTRESTED PLEASE SHARE TO SAYINI.PRASANTHI@SPSOFTGLOBAL.COM NOTE:ONLY SHORLISTED PROFILES WILL GET THE INTERVIEW CALLS.

Eligibility & process

Who can apply

Experience: 12-15 Yrs. Open to matching qualifications.

Skills & stack

Application Technical architecture Application security Artificial Intelligence Python Typescript Technical Javascript

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 (Application, Technical architecture, Application security, Artificial Intelligence, Python, Typescript, Technical, Javascript) & 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 SP Software 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 SP Software as a Applied AI Engineer?
Highlight SP Software's market reputation, recent tech innovations, and how your skills in Application 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.
SP Software Competitive Package (Best in Industry)
Recruiter Resume Apply
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