Staff Applied AI Engineer

This role is for one of the Weekday's clients

Salary range: Rs 8000000 - Rs 13000000 (ie INR 80-130 LPA)Min Experience: 10 years

Location: Remote (India)

JobType: full-time

As a Staff Applied AI Engineer, you will be responsible for building and productionizing advanced AI systems powered by Large Language Models (LLMs) and intelligent agents. You will work on mission-critical capabilities such as AI Assistants, Autonomous AI Agents, Deep Research Agents, Conversational Interfaces, Semantic Search, Search Personalization, and AI-driven automation—delivering features that directly impact millions of users’ productivity.

The mission of the AI Engineering team is to leverage large-scale data and cutting-edge AI to predict user behaviors, personalize experiences, and optimize every stage of the customer journey through intelligent automation.

Requirements

What You’Ll Be Working On

AI Assistant & Agent Systems

• Agent Architecture & Implementation: Build sophisticated multi-agent systems capable of reasoning, planning, and executing complex workflows.
• Context Management: Develop systems that maintain conversational context across multi-turn interactions.
• LLM & Agentic Platforms: Build scalable large language model and agentic platforms that drive adoption of AI-powered systems.
• Backend Systems: Develop scalable backend infrastructure to support AI assistants and agents.
• AI Features: Work on Conversational AI, Natural Language Search, Personalized Content Generation, and similar applications.

Classical AI/ML (Optional Focus)

• Search Scoring & Ranking: Enhance recommendation systems and search relevance algorithms.
• Entity Extraction: Build models for entity classification and automated keyword extraction.
• Lookalike & Recommendation Systems: Develop matching engines and intelligent suggestion systems.

Key Responsibilities

• Design & Deploy Production LLM Systems: Deliver scalable, reliable AI systems that serve millions of users.
• Agent Development: Create advanced AI agents that chain multiple LLM calls, integrate with APIs, and maintain state across workflows.
• Prompt Engineering: Design and optimize prompting strategies, balancing fine-tuning and advanced prompting techniques.
• System Integration: Build robust APIs and integrate AI features into existing infrastructure and external platforms.
• Evaluation & QA: Implement evaluation frameworks, A/B testing, and monitoring systems to ensure accuracy, reliability, and safety.
• Performance Optimization: Optimize systems for latency, scalability, and cost across different LLM providers.
• Collaboration: Partner with product teams, backend engineers, and stakeholders to translate business requirements into technical solutions.

Required Qualifications

Core Ai/Llm Experience

• 10+ years of software engineering experience focused on production systems.
• 1.5+ years (2023–present) of hands-on experience building real-world LLM-powered applications (GPT, Claude, Llama, or others).
• Proven experience in building customer-facing, scalable LLM applications beyond prototypes.
• Strong expertise in multi-step AI agent development, LLM chaining, and workflow automation.
• Deep understanding of prompt engineering, few-shot learning, and optimization techniques.

Technical Skills

• Expert-level Python programming for production AI systems.
• Strong backend engineering skills with scalable APIs and distributed architectures.
• Experience with LangChain, LlamaIndex, or similar frameworks.
• API integration expertise to enable advanced AI functionality.
• Experience deploying and managing AI systems in cloud environments (AWS, GCP, Azure).

Quality & Evaluation Focus

• Hands-on experience with evaluation frameworks for LLM systems (accuracy, safety, and performance).
• Strong knowledge of A/B testing and experimental design.
• Experience in production monitoring, debugging, and reliability engineering.
• Skilled in managing scalable data pipelines to support AI systems.

What Makes A Great Candidate

• Production-First Mindset: You’ve built and scaled AI systems used by real customers—not just research projects.
• Technical Depth with Business Impact: Ability to design end-to-end solutions while balancing cost, scalability, and performance trade-offs.
• Evaluation & Quality Excellence: Focus on measurable performance, safety, and user experience.
• Adaptability & Learning: Comfortable with ambiguity, rapidly evolving frameworks, and staying ahead of the AI landscape.

Skills

Large Language Models (LLMs) • Generative AI • Prompt Engineering • Multi-Agent Systems • Conversational AI • Chatbot Development