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REQUIREMENT_ID_347 • 3-DAY_ACTIVE_POLICY

Forward Deployed Engineer

Company
Company DehazeLabs
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary ₹2.16 LPA – ₹4.20 LPA
Location
Location Remote
Posted Date
Posted Date Yesterday
Python APIs microservices cloud (AWS/GCP) Docker Kubernetes data pipelines LLMs AI agents system design problem solving communication
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Aptitude Practice Questions Open Resource ↗
Curated quantitative and logical reasoning problems to sharpen the analytical skills needed for the initial screening test.
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Company Interview Preparation Guide Open Resource ↗
Comprehensive resource covering typical interview formats, common technical questions, and tips for presenting yourself effectively.
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Comprehensive Interview Prep Resource Open Resource ↗
Offers detailed explanations of core CS concepts, coding patterns, and mock interview experiences to boost confidence.
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Algorithm Problem Set Open Resource ↗
Extensive collection of coding problems to practice data structures, algorithms, and problem‑solving speed for the coding round.

Bachelor's degree (B.Tech/B.E./B.Sc./MCA) in Computer Science, Information Technology, Electronics, or related fields. Strong problem‑solving aptitude and willingness to work directly with enterprise customers.

1
Round 1: Online coding assessment (Data structures & algorithms)
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Round 2: Technical interview (system design, AI/ML concepts, past projects)
3
Round 3: HR interview (fit, motivation, cultural alignment)
DehazeLabs is an emerging AI‑focused services partner of Anthropic, dedicated to turning cutting‑edge large language models into practical, enterprise‑grade agents. The company works with a diverse set of businesses – from fintech to logistics – helping them automate complex workflows, improve decision‑making speed, and unlock new revenue streams through intelligent automation. With a small but highly skilled engineering team, DehazeLabs emphasizes rapid prototyping, close customer collaboration, and a relentless focus on measurable business impact. The culture is fast‑paced, merit‑based, and encourages engineers to wear multiple hats – from product discovery to production operations. As a Forward Deployed Engineer (FDE), you will sit at the intersection of engineering, product, and customer success. You will embed directly with client teams, spend time understanding their day‑to‑day processes, and translate vague operational challenges into concrete technical solutions. The role is ideal for fresh graduates who love to dive into real‑world problems, enjoy building end‑to‑end systems, and are excited about the practical applications of generative AI. You will own the entire lifecycle of an AI‑agent deployment – from discovery workshops, through prototype building, to production rollout, monitoring, and iterative improvement based on real‑world metrics. Key responsibilities include: 1. Conduct on‑site or virtual discovery sessions with enterprise customers to map existing workflows and identify automation opportunities. 2. Draft clear technical specifications and roadmaps that turn loosely defined problems into actionable engineering plans. 3. Design, develop, and integrate AI agents using Python, LangChain, FastAPI, and cloud services (AWS/GCP). 4. Build robust API integrations, data pipelines, and micro‑service architectures that connect the agents to customer tools such as CRM, ERP, and data warehouses. 5. Deploy solutions on Docker/Kubernetes clusters, ensuring scalability, reliability, and security. 6. Set up observability, logging, and alerting to monitor agent performance and business KPIs. 7. Conduct A/B testing with real‑world data, measure impact on efficiency or cost, and iterate on the solution. 8. Document reusable patterns, libraries, and best‑practice guides for future deployments. 9. Collaborate closely with DehazeLabs product and core engineering teams to feed back customer insights and influence platform roadmap. 10. Provide post‑deployment support, troubleshooting, and knowledge transfer to client technical teams. Tech Stack: Python, LangChain, FastAPI, Docker, Kubernetes, AWS/GCP, REST/GraphQL APIs, PostgreSQL/NoSQL, CI/CD pipelines, LLMs (Claude, GPT‑4), Git. Growth Path: Starting as a Junior Forward Deployed Engineer, you can progress to Senior FDE, then to Solutions Architect or Product Lead, eventually moving into leadership roles such as Head of Enterprise Solutions. The role offers exposure to both deep technical work and high‑impact business engagements, making it a fast track for ambitious engineers. Why Join DehazeLabs? You will work on the frontier of applied AI, solving tangible problems for Fortune‑500 clients while gaining hands‑on experience with the latest LLM technologies. The remote‑first policy gives you flexibility, and the small team environment ensures your contributions are visible and directly shape the company’s direction. If you are curious, customer‑obsessed, and eager to see your code drive real business outcomes, DehazeLabs provides the perfect launchpad.

DehazeLabs — AI & Machine Learning 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.

Open Resource ↗
COMPANY GUIDE 🎯
DehazeLabs Interview Preparation Corner

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

Open Resource ↗
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.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

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

Open Resource ↗
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, APIs, microservices, cloud (AWS/GCP), Docker, Kubernetes, data pipelines, LLMs, AI agents, system design, problem solving, communication) & 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
DehazeLabs Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
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).
Why do you want to join DehazeLabs as a Forward Deployed Engineer?
Preparation Tip: Highlight DehazeLabs'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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