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REQUIREMENT_ID_213 • 3-DAY_ACTIVE_POLICY

FDE intern

Company
Company Thinkly AI
Type
Opportunity Type Internship
Salary
Stipend / Salary Stipend: Rs 15,000 per month
Location
Location Remote
Posted Date
Posted Date Today
Python FastAPI Next.js PostgreSQL Vector DB N8N LangChain LLM prompting debugging AI voice APIs Git CI/CD
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Comprehensive placement paper collection Open Resource ↗
A curated set of past placement papers to practice problem‑solving and test-taking strategies relevant for the recruitment process.
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Interview experience repository Open Resource ↗
Real candidate experiences and question breakdowns that help you understand the interview flow and expectations.
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Preparation guide for tech interviews Open Resource ↗
Step‑by‑step study plan covering core topics, mock interviews, and tips to improve performance in technical rounds.
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Algorithm practice platform Open Resource ↗
A vast problem set to sharpen coding skills, covering data structures, algorithms, and system design fundamentals.

B.Tech/B.E./M.Tech or equivalent in Computer Science, Electronics, IT or related branches; minimum 60% aggregate (or 6.0 CGPA) throughout the degree; must be a final‑year student or have graduated within the last 2 years; no active backlogs at the time of joining; must be able to work 8 hours daily as per the internship schedule.

1
Round 1: Online coding test (focus on data structures and algorithms)
2
Round 2: Technical interview (system design, debugging, prompt engineering)
3
Round 3: HR interview (culture fit, internship logistics)
Thinkly Labs, operating under the brand Thinkly AI, is a fast‑growing Indian startup focused on building AI‑driven voice agents for the real‑estate sector. The company leverages large language models, vector databases, and cutting‑edge speech synthesis to create conversational agents that can handle inbound and outbound calls, qualify leads, and provide property information in real time. With a client roster that includes some of the biggest names in Indian real‑estate, Thinkly Labs has positioned itself as a niche player that bridges the gap between advanced AI research and tangible business outcomes. The work culture is highly collaborative, experimental, and driven by a "ship‑fast, iterate‑fast" mindset. Engineers get hands‑on exposure to production‑grade AI pipelines from day one, making it an ideal environment for fresh talent eager to see their code impact real customers. The role of a Forward Deployed Engineer (FDE) Intern is uniquely positioned at the intersection of client interaction, product engineering, and AI research. As an FDE intern, you will be embedded with client‑facing teams, debugging live voice agents during real calls, fine‑tuning model prompts, and shipping small yet critical features to the production stack. You will also build demo environments for prospective customers, which involves crafting prompts that are both technically sound and business‑relevant. The internship is full‑time, remote, and requires a commitment of 8 hours daily, Monday to Saturday, with occasional alternate Saturdays. Key responsibilities include: 1. Debugging and optimizing AI voice agents running on live client calls. 2. Implementing bug fixes and minor feature enhancements in the production codebase. 3. Designing and delivering interactive demos for sales prospects. 4. Writing, iterating, and engineering prompts that reliably guide LLM behavior. 5. Collecting field failures, reproducing them, and feeding insights back to the product team. 6. Collaborating with backend engineers to integrate vector‑DB retrieval and speech‑to‑text pipelines. 7. Maintaining documentation of client‑specific customizations. 8. Participating in code reviews and adhering to the team’s CI/CD practices. 9. Monitoring performance metrics of deployed agents and proposing improvements. 10. Assisting the support team with urgent client escalations. The tech stack revolves around Next.js for the frontend, FastAPI for backend services, PostgreSQL for relational data, various vector‑DB solutions for semantic search, and N8N for workflow automation. Familiarity with OpenAI, Anthropic, or Gemini APIs, as well as LangChain/LlamaIndex for prompt orchestration, is highly valued. Strong Python skills and an engineering mindset toward prompting are non‑negotiable. Growth prospects are significant: high‑performing interns can be offered full‑time roles, gain exposure to enterprise clients, and potentially lead product modules. The startup environment also offers rapid learning, mentorship from senior AI engineers, and the chance to influence product direction early in your career. Joining Thinkly AI means working on cutting‑edge conversational AI, solving real business problems, and building a portfolio that stands out in the Indian tech ecosystem.

Thinkly AI — Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

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COMPANY GUIDE 🎯
Thinkly AI Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Thinkly AI and off-campus tech roles.

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MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

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CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

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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 (Python, FastAPI, Next.js, PostgreSQL, Vector DB, N8N, LangChain, LLM prompting, debugging, AI voice APIs, Git, CI/CD) & 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
Thinkly AI Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
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.
Why do you want to join Thinkly AI as a FDE intern?
Preparation Tip: Highlight Thinkly AI's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: 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?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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