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GenAI Engineer

Modern Ballistic

  • Mumbai, India
  • Competitive Package (Best in Industry)
  • 5-10 Yrs
  • Posted Oct 23, 2026
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About the role

Role Summary We are looking for a Senior AI Developer / AI Lead with 5-8 years of experience to lead the design and development of enterprise-grade Generative AI and Conversational AI solutions. The role will have a strong hands-on focus on Python, GPT-based LLMs, RAG, AI-powered Knowledge Bases, Conversational AI, AI Evaluation Frameworks, and Microsoft Agent Framework. The candidate will also be responsible for providing technical direction, mentoring team members, and leading AI development initiatives from POC to production. Required Skills 58 years of experience in AI / Generative AI development. Strong hands-on experience with Python. Hands-on experience with the Microsoft Agent Framework. Strong experience working with LLMs, particularly GPT-based LLMs. Hands-on experience designing and implementing RAG (Retrieval-Augmented Generation) solutions. Experience building AI-powered Knowledge Bases and enterprise knowledge retrieval solutions. Strong experience developing Conversational AI / AI chatbot solutions. Strong understanding of Prompt Engineering and LLM application architecture. Experience with LLM / RAG assessment frameworks and defining assessment strategies. Understanding of evaluation parameters such as accuracy, relevance, groundedness/faithfulness, hallucination, and retrieval quality. Understanding of AI agents, tool calling, workflows, and agent orchestration. Experience integrating AI solutions with APIs, databases, enterprise applications, and knowledge repositories. Ability to design scalable and production-ready GenAI architectures. Experience providing technical leadership and mentoring developers. Ability to review code, define development standards, and guide the team on AI engineering best practices. Experience working directly with stakeholders to understand requirements and translate them into AI solutions. Good to Have Experience with Azure AI / Microsoft AI ecosystem. Strong understanding of embeddings, chunking, semantic search, reranking, and retrieval optimization. Experience taking GenAI POCs into production. Experience with AI application monitoring, evaluation, and observability. Knowledge of enterprise API and microservices architecture. Experience leading a small AI/GenAI development team. Exposure to multiple LLM providers and models. Leadership Expectations Lead the technical development of GenAI / Conversational AI solutions. Mentor and guide junior and mid-level AI developers. Break down requirements into technical tasks and development plans. Review solution architecture and code quality. Identify technical risks and recommend appropriate AI approaches. Collaborate with business, product, and engineering teams. Ensure AI solutions meet expected quality, performance, security, and scalability standards. Not Required Traditional Machine Learning experience is not mandatory. ML model training/development is not the focus of this role. LangGraph experience is not required .

Eligibility & process

Who can apply

Experience: 5-10 Yrs. Open to matching qualifications.

Skills & stack

Kernel Microsoft Python Framework Semantic Artificial Intelligence

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 (Kernel, Microsoft, Python, Framework, Semantic, Artificial Intelligence) & 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 Modern Ballistic 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 Modern Ballistic as a GenAI Engineer?
Highlight Modern Ballistic's market reputation, recent tech innovations, and how your skills in Kernel 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.
Modern Ballistic Competitive Package (Best in Industry)
Recruiter Resume Apply
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