Senior Applied AI Engineer

This role is for one of the Weekday's clients

Salary range: Rs 6000000 - Rs 9000000 (ie INR 60-90 LPA)Min Experience: 7 years

Location: Remote (India)

JobType: full-time

As a Senior Applied AI Engineer, you will be responsible for designing, building, and productionizing advanced AI systems powered by Large Language Models (LLMs) and intelligent agents. You’ll work on mission-critical capabilities such as AI Assistants, Autonomous Agents, Conversational Systems, Semantic Search, Search Personalization, Deep Research Agents, and AI-powered automation—driving measurable impact on user productivity at scale.

The role focuses on leveraging large-scale data and cutting-edge AI to predict user behaviors, personalize experiences, and optimize 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: Design systems to maintain context across multi-turn conversations.
• LLM & Agentic Platforms: Develop scalable agentic frameworks and large language model platforms to enable widespread adoption.
• Backend Systems: Build and maintain robust back-end infrastructure to support AI agents.
• AI Features: Deliver solutions like Conversational AI, Semantic Search, and Personalized Content Generation.

Classical AI/ML (Optional Focus)

• Enhance recommendation systems, search relevance, and entity extraction models.
• Build intelligent matching engines and lookalike/recommendation systems.

Key Responsibilities

• Design & Deploy LLM Systems: Deliver reliable, scalable production systems serving millions of users.
• Agent Development: Build advanced AI agents capable of chaining LLM calls, integrating APIs, and managing workflow states.
• Prompt Engineering: Develop, test, and optimize prompting strategies to maximize LLM effectiveness.
• System Integration: Create APIs and integrate AI features into existing infrastructures and external platforms.
• Evaluation & QA: Establish evaluation frameworks, monitoring systems, and A/B testing pipelines to ensure quality, safety, and reliability.
• Performance Optimization: Drive cost, latency, and scalability improvements across models and providers.
• Collaboration: Partner with product teams, backend engineers, and stakeholders to deliver business-driven AI solutions.

Required Qualifications

Core Ai/Llm Experience

• 7+ years of software engineering experience with production systems.
• 1.5+ years (2023–present) of hands-on experience building real-world LLM applications (GPT, Claude, Llama, etc.).
• Proven track record in delivering customer-facing, scalable LLM-powered products.
• Experience in multi-step agent development, workflow automation, and LLM chaining.
• Strong expertise in prompt engineering, few-shot learning, and optimization techniques.

Technical Engineering Skills

• Expert-level proficiency in Python for production systems.
• Strong background in backend engineering, APIs, and distributed architectures.
• Familiarity with frameworks like LangChain, LlamaIndex, or similar.
• Proven experience with API integrations and cloud deployment (AWS, GCP, Azure).

Quality & Evaluation

• Skilled in building evaluation frameworks for LLM accuracy, safety, and performance.
• Strong understanding of A/B testing methodologies.
• Hands-on experience with production monitoring, debugging, and reliability engineering.
• Experience managing data pipelines to support large-scale AI systems.

What Makes A Great Candidate

• Production-First Mindset: Experienced in building AI systems used by real users, not just prototypes.
• Technical Depth with Business Focus: Ability to design end-to-end systems, balancing performance, scalability, and cost trade-offs.
• Evaluation & Quality Excellence: Track record of implementing measurable evaluation methodologies while prioritizing safety and UX.
• Adaptability & Growth: Comfortable working in ambiguous spaces, staying current with the evolving AI landscape, and adapting to new models and frameworks.

Skills

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